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Chowdhury, Mohammad Noor Nawaz. "IntelWiki - Recommending Reference Materials in Context to Facilitate Editing Wikipedia." Springer, 2014. http://hdl.handle.net/1993/23592.
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Participation in contributing content to online communities remains heavily skewed. Yet little research has focused on lowering the contribution effort. I describe a general approach to facilitating user-generated content within the context of Wikipedia. I also present the IntelWiki prototype, a design and implementation of this approach, which aims to make it easier for users to create or enhance the free-form text in Wikipedia articles. The IntelWiki system i) recommends article-relevant reference materials, ii) draws the users' attention to key aspects of the recommendations, and iii) allows users to consult the recommended materials in context. A laboratory evaluation with 16 novice Wikipedia editors revealed that, in comparison to the default Wikipedia design, IntelWiki's approach has positive impacts on editing quantity and quality. Participants also reported experiencing significantly lower mental workload while editing with IntelWiki and preferred the new design.
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Dias, Pedro Ricardo Gomes. "Recommending media content based on machine learning methods." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/6581.
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Dissertação para obtenção do Grau de Mestre em
Engenharia InformáticaInformation is nowadays made available and consumed faster than ever before. This information technology generation has access to a tremendous deal of data and is left with the heavy burden of choosing what is relevant. With the increasing growth of media sources, the amount of content made available to users has become overwhelming and in need to be managed. Recommender systems emerged with the purpose of providing personalized and meaningful content recommendations based on users’ preferences and usage history. Due to their utility and commercial potential, recommender systems integrate many audiovisual content providers and represent one of their most important and valuable services. The goal of this thesis is to develop a recommender system based on matrix factorization methods, capable of providing meaningful and personalized product recommendations to individual users and groups of users, by taking into account users’ rating patterns and biased tendencies, as well as their fluctuations throughout time.
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Maes, Pauline. "Engaging Content Experience- Utilizing the Strossle recommendation capabilities, across publishers’ websites." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-21487.
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The project aims at exploring the process of designing recommender systems from a users’ perspective. Recommendations are the systems that can help users navigate in the overload of information, that is currently available online. This project focuses on the recommender network of Strossle, which provides article recommendations across various publishers’ websites. User-centered research has been performed to understand the current system and how that influences the users’ perceived experience. The goal was to develop a more engaging content experience for the Strossle recommendation system. This is done by means of participatory design methods. As people tend to use recommendations very sporadic and they often do not really know what they are looking for. The emphasis was on finding the balance between exploratory browsing and navigating towards the users’ preferences. In order to achieve this, a more dynamic widget has been developed that offers navigation in various related topics.
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Belin, Kirsten, and Yi Hsin Wang. "Job Adverts á la 2010 : A study of content, style, recommendations and students thoughts and perceptions." Thesis, Örebro universitet, Akademin för humaniora, utbildning och samhällsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-12149.
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This thesis is a study of Swedish job adverts published on recruitment websites. The thesis has a qualitative approach and is including three parts. The first part is an analysis of 50 job advert to create an understanding for what a job advert looks like today, 2010. This part study resulted in a prototype of a typical Swedish job advert published on recruitment web-sites 2010. The second part is a literature study in order to find out what the recommenda-tions from experts for writing job adverts are at present. This step generated a list of 21 kinds of advice that the experts recommend that one should think of when writing a job advert. The last part consists of group interviews with Swedish speaking business admini-stration students about their thoughts and perceptions of content in job adverts. The result shows that the respondents preferred job adverts that were branded (informed the applicant of the employer). They thought that the job adverts were very stereotype and written in a cliché language. And they also preferred job adverts that contained information about what employer could offer an employee apart from the job itself (in non financial terms). It was also clear that the students were reading in and interpreting a lot meaning behind the words. As an overall conclusion the study suggests that there is food for thought when it comes to how job adverts are being written in Sweden in 2010 and that the field of effec-tiveness of job adverts is in serious need of more research.
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Andersson, Morgan. "Personal news video recommendations based on implicit feedback : An evaluation of different recommender systems with sparse data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234137.
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The amount of video content online will nearly triple in quantity by 2021 compared to 2016. The implementation of sophisticated filters is of paramount importance to manage this information flow. The research question of this thesis asks to what extent it is possible to generate personal recommendations, based on the data that news videos implies. The objective is to evaluate how different recommender systems compare to complete random, each other and how they are received by users in a test environment. This study was performed during the spring of 2018, and explore four different algorithms. These recommender systems include a content-based, a collaborative-filter, a hybrid model and a popularity model as a baseline. The dataset originates from a news media startup called Newstag, who provide video news on a global scale. The data is sparse and includes implicit feedback only. Three offline experiments and a user test were performed. The metric that guided the algorithms offline performance was their recall at 5 and 10, due to the fact that the top list of recommended items are of most interest. A comparison was done on different amounts of meta-data included during training. Another test explored respective algorithms performance as the density of the data increased. In the user test, a mean opinion score was calculated based on the quality of recommendations that each of the algorithms generated for the test subjects. The user test also included randomly sampled news videos to compare with as a baseline. The results indicate that for this specific setting and data set, the content-based recommender system performed best in both the recall at five and ten, as well as in the user test. All of the algorithms outperformed the random baseline.Mängden video som finns tillgänglig på internet förväntas att tredubblas år 2021 jämfört med 2016. Detta innebär ett behov av sofistikerade filter för att kunna hantera detta informationsflöde. Detta examensarbete ämnar att svara på till vilken grad det går att generera personliga rekommendationer baserat på det data som nyhetsvideo innebär. Syftet är att utvärdera och jämföra olika rekommendationssystem och hur de står sig i ett användartest. Studien utfördes under våren 2018 och utvärderar fyra olika algoritmer. Dessa olika rekommendationssystem innefattar tekniker som content-based, collaborative-filter, hybrid och en popularitetsmodell används som basvärde. Det dataset som används är glest och har endast implicita attribut. Tre experiment utförs samt ett användartest. Mätpunkten för algoritmernas prestanda utgjordes av recall at 5 och recall at 10, dvs. att man mäter hur väl algoritmerna lyckas generera värdefulla rekommendationer i en topp-fem respektive topp-10-lista av videoklipp. Detta då det är av intresse att ha de mest relevanta videorna högst upp i sin lista av resultat. En jämförelse gjordes mellan olika mängd metadata som inkluderades vid träning. Ett annat test gick ut på att utforska hur algoritmerna presterar då datasetet blir mindre glest. I användartestet användes en utvärderingsmetod kallad mean-opinion-score och denna räknades ut per algoritm genom att testanvändare gav betyg på respektive rekommendation, baserat på hur intressant videon var för dem. Användartestet inkluderade även slumpmässigt generade videos för att kunna jämföras i form av basvärde. Resultaten indikerar, för detta dataset, att algoritmen content-based presterar bäst både med hänsyn till recall at 5 & 10 samt den totala poängen i användartestet. Alla algoritmer presterade bättre än slumpen.
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Дячук, Іван Сергійович. "Інтелектуальна система підбору клієнтського контенту." Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/25528.
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Магістерська дисертація містить результати розроблення інтелектуальної систми підбору клієнтського контенту, що можуть бути використані як основа для реалізації аналогічних рішень. В роботі розроблено комбіновану математичну модель та програмний комплекс з її використанням. Результати роботи були використані при розробці системи, що впроваджена в експлуатацію, що підтверджує практичне значення одержаних результатів.The master’s thesis contains the results of the development of intellectual system of selection of client content that can be used as a basis for the implementation of similar solutions. In the work the combined mathematical model and software complex with its use are developed. The results of the work were used in the development of system being put into operation, confirming the practical value of the results that were obtained.
Магистерская диссертация содержит результаты разработки интеллектуальной системы подбора клиентского контента, которые могут быть использованы, как основа для реализации аналогичных решений. В работе разработана комбинированная математическая модель и программный комплекс с ее использованием. Результаты работы были использованы при разработке системы, внедренной в эксплуатацию, что подтверждает практическое значение полученных результатов.
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Angelovska, Marina. "Content-based Recommender System for Detecting Complementary Products : Evaluating Siamese Neural Networks for Predicting Complementary Relationships among E-Commerce Products." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280455.
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As much as the diverse and rich offer on e-commerce websites helps the users find what they need at one market place, the online catalogs are sometimes too overwhelming. Recommender systems play an important role in e-commerce websites as they improve the customer journey by helping the users find what they want at the right moment. These recommendations can be based on users’ characteristics, demographics, purchase or session history.In this thesis we focus on identifying complementary relationship between products in the case of the largest e-commerce company in the Netherlands. Complementary products are products that go well together, products that might be a necessity to the chosen product or simply a nice addition to it. At the company, there is big potential as complementary products increase the average purchase value and they exist for less than 20% of the whole catalog.We propose a content-based recommender system for detecting complemen- tary products, using a supervised deep learning approach that relies on Siamese Neural Network (SNN).The purpose of this thesis is three-fold; Firstly, the main goal is to create a SNN model that will be able to predict complementary products for any given product based on the content. For this purpose, we implement and compare two different models: Siamese Convolutional Neu- ral Network and Siamese Long Short-Term Memory (LSTM) Recurrent Neural Network. We feed these neural networks with pairs of products taken from the company, which are either complementary or non-complementary. Secondly, the basic assumption of our approach is that most of the important features for a product are included in its title, but we conduct experiments including the product description and brand as well. Lastly, we propose an extension of the SNN approach to handle millions of products in a matter of seconds.∼As a result from the experiments, we conclude that Siamese LSTM can predict complementary products with highest accuracy of 85%. Our assumption that the title is the most valuable attribute was confirmed. In addition, trans- forming our solution to a K-nearest-neighbour problem in order to optimize it for millions of products gave promising results.Så mycket som det mångfaldiga och rika utbudet på e-handelswebbplatser hjälper användarna att hitta det de behöver på en marknadsplats, är online- katalogerna ibland för överväldigande. Rekommendationssystem en viktig roll på e-handelswebbplatser eftersom de förbättrar kundupplevelsen genom att hjälpa användarna att hitta vad de vill ha i rätt ögonblick. Dessa rekommen- dationer kan baseras på användarens egenskaper, demografi, inköps- eller ses- sionshistorik.I denna avhandling fokuserar vi på att identifiera komplementära förhållanden mellan produkter för det största e-handelsföretaget i Nederländerna. Komplet- terande produkter är produkter passar väl ihop, produkter som kan vara en nödvändighet för den valda produkten eller helt enkelt ett trevligt tillskott till den. På företaget finns det stor potential eftersom kompletterande produkter ökar det genomsnittliga inköpsvärdet och de tillhandahålls för mindre än 20% av hela katalogen.Vi föreslår ett innehållsbaserat rekommendationssystem för att upptäcka kom- pletterande produkter, med en övervakad strategi för inlärning som bygger på Siamese Neural Network (SNN). Syftet med denna avhandling är i tre steg; För det första är huvudmålet att skapa en SNN-modell som kan förutsäga komplet- terande produkter för en given produkt baserat på innehållet. För detta ändamål implementerar och jämför vi två olika modeller: Siamese Convolutional Neu- ral Network och Siamese Long Short-Term Memory (LSTM) Recurrent Neural Network. Vi matar in data i dessa neurala nätverk med par produkter hämta- de från företaget, som antingen är komplementära eller icke-komplementära. Det andra grundläggande antagandet av vår metod att de flesta av de viktiga funktionerna för en produkt ingår i dess titel, men vi genomför också expe- riment inklusive produktbeskrivningen och varumärket. Slutligen föreslår vi en utvidgning av SNN-metoden för att hantera miljoner produkter på några sekunder.∼Som ett resultat av eperimenten drar vi slutsatsen att Siamese LSTM kan för- utsäga komplementära produkter med högsta noggrannhet på 85%. Vårt antagande att titeln är det mest värdefulla attributet bekräftades. Därtill är om- vandling av vår lösning till ett K-närmaste grannproblem för att optimera den för miljontals produkter gav lovande resultat.
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Codina, Busquet Victor. "Exploiting distributional semantics for content-based and context-aware recommendation." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/277574.
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During the last decade, the use of recommender systems has been increasingly growing to the point that, nowadays, the success of many well-known services depends on these technologies. Recommenders Systems help people to tackle the choice overload problem by effectively presenting new content adapted to the user¿s preferences. However, current recommendation algorithms commonly suffer from data sparsity, which refers to the incapability of producing acceptable recommendations until a minimum amount of users¿ ratings are available for training the prediction models.
This thesis investigates how the distributional semantics of concepts describing the entities of the recommendation space can be exploited to mitigate the data-sparsity problem and improve the prediction accuracy with respect to state-of-the-art recommendation techniques. The fundamental idea behind distributional semantics is that concepts repeatedly co-occurring in the same context or usage tend to be related. In this thesis, we propose and evaluate two novel semantically-enhanced prediction models that address the sparsity-related limitations: (1) a content-based approach, which exploits the distributional semantics of item¿s attributes during item and user-profile matching, and (2) a context-aware recommendation approach that exploits the distributional semantics of contextual conditions during context modeling. We demonstrate in an exhaustive experimental evaluation that the proposed algorithms outperform state-of-the-art ones, especially when data are sparse.
Finally, this thesis presents a recommendation framework, which extends the widespread machine learning library Apache Mahout, including all the proposed and evaluated recommendation algorithms as well as a tool for offline evaluation and meta-parameter optimization. The framework has been developed to allow other researchers to reproduce the described evaluation experiments and make new progress on the Recommender Systems field easierDurant l'última dècada, l'ús dels sistemes de recomanació s'ha vist incrementat fins al punt que, actualment, l'èxit de molts dels serveis web més coneguts depèn en aquesta tecnologia. Els Sistemes de Recomanació ajuden als usuaris a trobar els productes o serveis que més s¿adeqüen als seus interessos i preferències. Una gran limitació dels algoritmes de recomanació actuals és el problema de "data-sparsity", que es refereix a la incapacitat d'aquests sistemes de generar recomanacions precises fins que un cert nombre de votacions d'usuari és disponible per entrenar els models de predicció. Per mitigar aquest problema i millorar així la precisió de predicció de les tècniques de recomanació que conformen l'estat de l'art, en aquesta tesi hem investigat diferents maneres d'aprofitar la semàntica distribucional dels conceptes que descriuen les entitats que conformen l'espai del problema de la recomanació, principalment, els objectes a recomanar i la informació contextual. En la semàntica distribucional s'assumeix la següent hipotesi: conceptes que coincideixen repetidament en el mateix context o ús tendeixen a estar semànticament relacionats. Concretament, en aquesta tesi hem proposat i avaluat dos algoritmes de recomanació que fan ús de la semàntica distribucional per mitigar el problem de "data-sparsity": (1) un model basat en contingut que explota les similituds distribucionals dels atributs que representen els objectes a recomanar durant el càlcul de la correspondència entre els perfils d'usuari i dels objectes; (2) un model de recomanació contextual que fa ús de les similituds distribucionals entre condicions contextuals durant la representació del context. Mitjançant una avaluació experimental exhaustiva dels models de recomanació proposats hem demostrat la seva efectivitat en situacions de falta de dades, confirmant que poden millorar la precisió d'algoritmes que conformen l'estat de l'art. Finalment, aquesta tesi presenta una llibreria pel desenvolupament i avaluació d'algoritmes de recomanació com una extensió de la llibreria de "Machine Learning" Apache Mahout, àmpliament utilitzada en el camp del Machine Learning. La nostra extensió inclou tots els algoritmes de recomanació avaluats en aquesta tesi, així com una eina per facilitar l'avaluació experimental dels algoritmes. Hem desenvolupat aquesta llibreria per facilitar a altres investigadors la reproducció dels experiments realitzats i, per tant, el progrés en el camp dels Sistemes de Recomanació.
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Kanard, M. Elizabeth. "Weighing in : an analysis of the NASW's web-based content regarding theoretical issues and practice recommendations for social workers working with overweight and obese individuals : a project based upon an independent investigation /." View online, 2008. http://hdl.handle.net/10090/5903.
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Gibała, Karolina, and Aleksandra Gujda. "The role of peer-created content in digital advertising : Perceptions of sponsored and non-sponsored recommendations on Instagram, its recognition as a product advertisement and its effects on the level of trustworthiness." Thesis, Högskolan i Jönköping, Internationella Handelshögskolan, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-39761.
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Background: Changing interests and expectations of societies have resulted in the development of new communication and business channels. The boom of social media, allowed for a rapid exchange of beliefs, opinions and ideas. The ways of advertising products have also been changed. Once beloved, both by companies and customers, celebrity endorsement is becoming less and less popular in favor of, commonly now used, peer-created reviews. Modern enterprises increasingly use an image of an ‘ordinary’ person in their marketing strategies and the internet has seen a flourishing trend of peercreated posts and reviews. The concept of trust has been known in the literature for ages, however with new times, new angles of perception of this phenomenon appear. There is still little to none research done in the area of trustworthiness towards peer-created content, and exploring this phenomenon is the purpose of this work. Purpose: The purpose of our work is in a way twofold. First, we aim to obtain more insights on how sponsored and non-sponsored peer-created content posted on Instagram can influence consumer’s knowledge of persuasion in advertising. Second, we examine if and how the source credibility and the trustworthiness of shared content can be affected by customers awareness of the persuasion intent of sponsored texts. Method: Web-based, self-completion surveys were disseminated amongst our friends and family in order to collect the data. The analysis was done through SPSS, using the correlation and multiple regression analysis calculations. Furthermore, to deeper understand the relationships between the variables and to find possible interaction effects between them, the moderator analysis was conducted. Conclusion: The sponsored peer-created content of the post is widely recognized as a deliberate marketing activity of a company. What is more, the non-sponsored content is also being perceived as a product advertisement, however, with less conviction that in the case of the sponsored posts. A positive relationship between lack of recognition of non-sponsored posts as the product advertisements and the trustworthiness towards the content has been found, meaning that the less the content of the post is perceived as a deliberate marketing activity of a company, the more trustworthy it is to the respondent. Consequently, the more people perceive a specific post as an advertisement commissioned by the company, the less trustworthy they feel towards its content.
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Geoghegan, Mary Denise. "A Review of whether peri-operative nursing records used in the Western Cape Metropolitan health region are in line with international standards and recommendations for standard content and design characteristics for the Western Cape." Master's thesis, University of Cape Town, 2000. http://hdl.handle.net/11427/2948.
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Bibliography: leaves 180-193.Peri-operative nursing is faced with increasing pressure to improve productivity while coping with diminishing resources. Nurses have to work harder and faster while still maintaining a high standard of patient care. This emphasises the need for comprehensive, yet easy-to-use peri-operative nursing records. A descriptive, non experimental research design was used to survey peri-operative nursing records used in the Western Cape Metropolitan Health Region and content and design characteristics were identified. A comparison was made between these records and the standard set by the Association of Operating Room Nurses (AORN) in the United States of America. The criteria stipulated by the AORN standard were found to be relevant to South African peri-operative nursing practice with a few exceptions. In spite of this, the perioperative nursing records reviewed did not compare well with the AORN standard and were particularly deficient in risk management areas such as potential injury related to positioning the patient, and electrical and physical hazards. Content criteria not mentioned by the standard, but appearing in the local records were identified and certain aspects of design recognised in the literature were also discussed. Recommendations for a South African standard for peri-operative nursing records were made, as well a$ recommendations for further research into the use and design of peri-operative nursing records.
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Paireekreng, Worapat. "An integrated mobile content recommendation system." Thesis, Paireekreng, Worapat (2012) An integrated mobile content recommendation system. PhD thesis, Murdoch University, 2012. https://researchrepository.murdoch.edu.au/id/eprint/13599/.
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Many features have been added to mobile devices to assist the user's information consumption. However, there are limitations due to information overload on the devices, hardware usability and capacity. As a result, content filtering in a mobile recommendation system plays a vital role in the solution to this problem. A system that utilises content filtering can recommend content which matches a user's needs based on user preferences with a higher accuracy rate.
However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. The problems can be viewed as first time connection and first content rating for non-interactive recommendation systems where information is insufficient to predict mobile content which will match with a user's needs. In addition, how to find relevant items for the content recommendation system which are related to a user's profile is also a concern.
An integrated model that combines the user group identification and mobile content filtering for mobile content recommendation was proposed in this study in order to address the current limitations of the mobile content recommendation system. The model enhances the system by finding the relevant content items that match with a user's needs based on the user's profile. A prototype of the client-side user profile modelling is also developed to demonstrate the concept.
The integrated model applies clustering techniques to determine groups of users. The content filtering implemented classification techniques to predict the top content items. After that, an adaptive association rules technique was performed to find relevant content items. These approaches can help to build the integrated model.
Experimental results have demonstrated that the proposed integrated model performs better than the comparable techniques such as association rules and collaborative filtering. These techniques have been used in several recommendation systems. The integrated model performed better in terms of finding relevant content items which obtained higher accuracy rate of content prediction and predicted successful recommended relevant content measured by recommendation metrics. The model also performed better in terms of rules generation and content recommendation generation. Verification of the proposed model was based on real world practical data. A prototype mobile content recommendation system with client-side user profile has been developed to handle the revisiting user issue. In addition, context information, such as time-of-day and time-of-week, could also be used to enhance the system by recommending the related content to users during different time periods.
Finally, it was shown that the proposed method implemented fewer rules to generate recommendation for mobile content users and it took less processing time. This seems to overcome the problems of first time connection and first content rating for non-interactive recommendation systems.
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Smaaberg, Simen Fivelstad. "Context-Aware Group Recommendation Systems." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-27328.
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For a group of friends going to a concert or a festival, finding concerts that everyone is happy with can be challenging as everyone have their own preferences and wishes when it comes to music.In this thesis, a prototype of a group recommendation system for concerts is presented to solve this issue. The prototype is context sensitive; it takes a user's location and time into account when giving recommendations. The prototype implements three algorithms to recommend concerts by taking advantage of what users have listened to before: a collaborative filtering algorithm (k-Nearest Neighbor), a Matrix Factorization algorithm, and a Hybrid approach of these two.The thesis was written following the Design Science Research paradigm. The thesis covers the design and implementation of the prototype in addition to a brief review of the state of the art of the recommendation systems literature. The usability of the prototype was evaluated using the System Usability Scale, and a user centered evaluation was performed to evaluate the quality of recommendations. The results from the usability evaluation shows that users generally were satisfied with the usability of the prototype. The results from the Quality Evaluation shows that the k-Nearest Neighbor and Hybrid approach produces satisfactory results whereas the Matrix Factorization implementation is lagging a bit behind. The users testing the prototype were generally satisfied with the quality of recommendations, however further evaluation is needed to draw any final conclusions.
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Kirmemis, Oznur. "Openmore: A Content-based Movie Recommendation System." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609479/index.pdf.
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The tremendous growth of Web has made information overload problem increasingly serious. Users are often confused by huge amount of information available on the internet and they are faced with the problem of finding the most relevant information
that meets their needs. Recommender systems have proven to be an important solution approach to this problem. This thesis will present OPENMORE, a movie recommendation system, which is primarily based on content-based filtering technique. The distinctive point of this study lies in the methodology used to
construct and update user and item profiles and the optimizations used to fine-tune the constructed user models. The proposed system arranges movie content data as features of a set of dimension slots, where each feature is assigned a stable feature weight regardless of individual movies. These feature weights and the explicit feedbacks provided by the user are then used to construct the user profile, which is fine-tuned through a set of optimization mechanisms. Users are enabled to view their profile, update them and create multiple contexts where they can provide negative and positive feedback for the movies on the feature level.
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SILVA, Douglas Véras e. "CD-cars: cross domain context-aware recomender systems." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/18356.
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Traditionally, single-domain recommender systems (SDRS) have achieved good results in recommending relevant items for users in order to solve the information overload problem. However, cross-domain recommender systems (CDRS) have emerged aiming to enhance SDRS by achieving some goals such as accuracy improvement, diversity, addressing new user and new item problems, among others. Instead of treating each domain independently, CDRS use knowledge acquired in a source domain (e.g. books) to improve the recommendation in a target domain (e.g. movies). Likewise SDRS research, collaborative filtering (CF) is considered the most popular and widely adopted approach in CDRS, because its implementation for any domain is relatively simple. In addition, its quality of recommendation is usually higher than that of content-based filtering (CBF) algorithms. In fact, the majority of the cross-domain collaborative filtering RS (CD-CFRS) can give better recommendations in comparison to single domain collaborative filtering recommender systems (SD-CFRS), leading to a higher users’ satisfaction and addressing cold-start, sparsity, and diversity problems. However, CD-CFRS may not necessarily be more accurate than SD-CFRS. On the other hand, context-aware recommender systems (CARS) deal with another relevant topic of research in the recommender systems area, aiming to improve the quality of recommendations too. Different contextual information (e.g., location, time, mood, etc.) can be leveraged in order to provide recommendations that are more suitable and accurate for a user depending on his/her context. In this way, we believe that the integration of techniques developed in isolation (cross-domain and contextaware) can be useful in a variety of situations, in which recommendations can be improved by information from different sources as well as they can be refined by considering specific contextual information. In this thesis, we define a novel formulation of the recommendation problem, considering both the availability of information from different domains (source and target) and the use of contextual information. Based on this formulation, we propose the integration of cross-domain and context-aware approaches for a novel recommender system (CD-CARS). To evaluate the proposed CD-CARS, we performed experimental evaluations through two real datasets with three different contextual dimensions and three distinct domains. The results of these evaluations have showed that the use of context-aware techniques can be considered as a good approach in order to improve the cross-domain recommendation quality in comparison to traditional CD-CFRS.
Tradicionalmente, “sistemas de recomendação de domínio único” (SDRS) têm alcançado bons resultados na recomendação de itens relevantes para usuários, a fim de resolver o problema da sobrecarga de informação. Entretanto, “sistemas de recomendação de domínio cruzado” (CDRS) têm surgido visando melhorar os SDRS ao atingir alguns objetivos, tais como: “melhoria de precisão”, “melhor diversidade”, abordar os problemas de “novo usuário” e “novo item”, dentre outros. Ao invés de tratar cada domínio independentemente, CDRS usam conhecimento adquirido em um domínio fonte (e.g. livros) a fim de melhorar a recomendação em um domínio alvo (e.g. filmes). Assim como acontece na área de pesquisa sobre SDRS, a filtragem colaborativa (CF) é considerada a técnica mais popular e amplamente utilizada em CDRS, pois sua implementação para qualquer domínio é relativamente simples. Além disso, sua qualidade de recomendação é geralmente maior do que a dos algoritmos baseados em filtragem de conteúdo (CBF). De fato, a maioria dos “sistemas de recomendação de domínio cruzado” baseados em filtragem colaborativa (CD-CFRS) podem oferecer melhores recomendações em comparação a “sistemas de recomendação de domínio único” baseados em filtragem colaborativa (SD-CFRS), aumentando o nível de satisfação dos usuários e abordando problemas tais como: “início frio”, “esparsidade” e “diversidade”. Entretanto, os CD-CFRS podem não ser mais precisos do que os SD-CFRS. Por outro lado, “sistemas de recomendação sensíveis à contexto” (CARS) tratam de outro tópico relevante na área de pesquisa de sistemas de recomendação, também visando melhorar a qualidade das recomendações. Diferentes informações contextuais (e.g. localização, tempo, humor, etc.) podem ser utilizados a fim de prover recomendações que são mais adequadas e precisas para um usuário dependendo de seu contexto. Desta forma, nós acreditamos que a integração de técnicas desenvolvidas separadamente (de “domínio cruzado” e “sensíveis a contexto”) podem ser úteis em uma variedade de situações, nas quais as recomendações podem ser melhoradas a partir de informações obtidas em diferentes fontes além de refinadas considerando informações contextuais específicas. Nesta tese, nós definimos uma nova formulação do problema de recomendação, considerando tanto a disponibilidade de informações de diferentes domínios (fonte e alvo) quanto o uso de informações contextuais. Baseado nessa formulação, nós propomos a integração de abordagens de “domínio cruzado” e “sensíveis a contexto” para um novo sistema de recomendação (CD-CARS). Para avaliar o CD-CARS proposto, nós realizamos avaliações experimentais através de dois “conjuntos de dados” com três diferentes dimensões contextuais e três domínios distintos. Os resultados dessas avaliações mostraram que o uso de técnicas sensíveis a contexto pode ser considerado como uma boa abordagem a fim de melhorar a qualidade de recomendações de “domínio cruzado” em comparação às recomendações de CD-CFRS tradicionais.
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Rodas, Britez Marcelo Dario. "A Content-Based Recommendation System for Leisure Activities." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/242958.
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People’s selection of leisure activities is a complex choice because of implicit human factors and explicit environmental factors. Satisfactory participation in leisure activities is an important task since keeping a regular active lifestyle can help to maintain and improve the wellbeing of people. Technology could help in selecting the most appropriate activities by designing and implementing activities, collecting people profiles and their preferences relations. In fact, recommendation systems, have been successfully used in the last years in similar tasks with different types of recommendation systems. This thesis aims at the design, implementation, and evaluation of recommendation systems that could help us to better understand the complex choice of selecting leisure activities. In this work, we first define an evaluation framework for different recommendations systems. Then we compare their performances using different evaluation metrics. Thus, we explore and try to better understand the user’s preferences over leisure activities. After, having a comprehensive analysis of modelling recommended items and leisure activities, we also design and implement a content-based leisure activity recommendation system to make use of a taxonomy of activities. Moreover, in the course of our research, we have collected and evaluated two datasets obtained one from the Meetup social network and the other from crowd-workers and made them available as open data sources for further evaluation in the recommendation system research community.
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Reis, Gustavo Henrique da Rocha. "CALearning - recomendação híbrida de conteúdos educacionais." Universidade Federal de Juiz de Fora, 2015. https://repositorio.ufjf.br/jspui/handle/ufjf/118.
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O uso de dispositivos m oveis v^em aumentando signi cativamente nos ultimos anos. Outra tend^encia e a consolida c~ao no uso Tecnologias Digitais da Informa c~ao e Comunica c~ao para ns educacionais. Estes cen arios juntos possibilitam novas formas de comunica c~ao entre professores e alunos, como, por exemplo, a recomenda c~ao de conte udos educacionais e colabora c~ao utilizando dispositivos m oveis. Este trabalho mostra uma arquitetura, chamada CALearning, que re une as principais caracter sticas para um sistema de aprendizado m ovel, como promover a colabora c~ao (recomenda c~ao e avalia c~ao de conte udos) entre os alunos e fazer a recomenda c~ao de conte udos de acordo com o estilo de aprendizagem e prefer^encias do usu ario. A arquitetura tamb em faz uso de informa c~oes de contexto para recomendar conte udos adaptados de acordo com as caracter sticas de acesso a Internet (taxa de transmiss~ao) e deslocamento do aluno durante sua intera c~ao com o aplicativo. Como prova de conceito foi desenvolvido tr^es sistemas chamados CALearningDroid, CALearningWeb e CALearningWS, baseado na arquitetura proposta
The use of mobile devices have increased signi cantly in recent years. Another trend is the consolidation in using Digital Technologies of Information and Communication for educational purposes. These scenarios together enable new forms of communication between teachers and students, for example, the recommendation of educational content and collaboration using mobile devices. This work shows an architecture called CALearning, which brings together the main features for a mobile learning system as promote collaboration (recommendation and content evaluation) among students and to do the content recommendation according to the learning style and user's preferences. The architecture also does use of context information to recommend content tailored according to the Internet access features (transmission rate) and displacement of the learner during their interaction with the application. As proof of concept, was developed three systems called CALearningDroid, CALearningWeb and CALearningWS, based in the proposed architecture.
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VASILOUDIS, THEODOROS. "Extending recommendation algorithms bymodeling user context." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-156306.
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Recommender systems have been widely adopted by onlinee-commerce websites like Amazon and music streaming services like Spotify. However, most research efforts have not sufficiently considered the context in which recommendations are made, especially when the input is implicit. In this work, we investigate the value of including contextual information like day-of-week in collaborative filtering recommender systems. For the investigation, we first implemented two algorithms, namely contextual prefiltering and contextual post-filtering. Then, we evaluated these algorithms with user data collected from Spotify. Experiment results show that the pre-filtering algorithm shows some promise against an item similarity baseline, indicating that further investigation could be rewarding. The post-filtering algorithm underperforms a popularity-derived baseline, due to information loss in the recommendationprocess.
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Jiang, Hao, and 江浩. "Personalized web search re-ranking and content recommendation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/197548.
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In this thesis, I propose a method for establishing a personalized recommendation system for re-ranking web search results and recommending web contents. The method is based on personal reading interest which can be reflected by the user’s dwell time on each document or webpage. I acquire document-level dwell times via a customized web browser, or a mobile device. To obtain better precision, I also explore the possibility of tracking gaze position and facial expression, from which I can determine the attractiveness of different parts of a document.
Inspired by idea of Google Knowledge Graph, I also establish a graph-based ontology to maintain a user profile to describe the user’s personal reading interest. Each node in the graph is a concept, which represents the user’s potential interest on this concept. I also use the dwell time to measure concept-level interest, which can be inferred from document-level user dwell times. The graph is generated based on the Wikipedia. According to the estimated concept-level user interest, my algorithm can estimate a user’s potential dwell time over a new document, based on which personalized webpage re-ranking can be carried out. I compare the rankings produced by my algorithm with rankings generated by popular commercial search engines and a recently proposed personalized ranking algorithm. The results clearly show the superiority of my method.
I also use my personalized recommendation framework in other applications. A good example is personalized document summarization. The same knowledge graph is employed to estimate the weight of every word in a document; combining with a traditional document summarization algorithm which focused on text mining, I could generate a personalized summary which emphasize the user’s interest in the document. To deal with images and videos, I present a new image search and ranking algorithm for retrieving unannotated images by collaboratively mining online search results, which consists of online images and text search results. The online image search results are leveraged as reference examples to perform content-based image search over unannotated images. The online text search results are used to estimate individual reference images’ relevance to the search query as not all the online image search results are closely related to the query. Overall, the key contribution of my method lies in its ability to deal with unreliable online image search results through jointly mining visual and textual aspects of online search results. Through such collaborative mining, my algorithm infers the relevance of an online search result image to a text query. Once I estimate a query relevance score for each online image search result, I can selectively use query specific online search result images as reference examples for retrieving and ranking unannotated images. To explore the performance of my algorithm, I tested it both on a standard public image datasets and several modestly sized personal photo collections. I also compared the performance of my method with that of two peer methods. The results are very positive, which indicate that my algorithm is superior to existing content-based image search algorithms for retrieving and ranking unannotated images. Overall, the main advantage of my algorithm comes from its collaborative mining over online search results both in the visual and the textual domains.published_or_final_version
Computer Science
Doctoral
Doctor of Philosophy
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Horsburgh, Ben. "Integrating content and semantic representations for music recommendation." Thesis, Robert Gordon University, 2013. http://hdl.handle.net/10059/859.
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Music recommender systems are used by millions of people every day to discover new and exciting music. Central to making recommendations is the representation of each track, which may be used to calculate similarity. Content representations capture the musical and texture facets of each track, and semantic representations describe social and cultural information provided by listeners. This thesis is motivated by an analysis of the strengths and weaknesses of both content and semantic representations. Content representations can be available for all tracks in a collection, but provide poor recommendation quality. Semantic representations suffer from the cold-start problem and are not available for all tracks, but provide good recommendation quality when a strong representation is available. These observations highlight the need to integrate both content and semantic representations, and use the strengths of each to improve music recommendation quality and discovery. A bridge of the gap between content and semantic representations is achieved in this thesis through hybrid representation. Content texture representations are examined, and a new music-inspired texture representation is defined. This content is integrated with semantic tags directly, and through a mid-level pseudo-tag representation. The effect of these approaches is to increase the high quality discovery of tracks, and to allow users to uncover interesting novel recommendations. The challenge of evaluating music recommendations when many tracks are undertagged is addressed. Implicit and explicit feedback provided by users is exploited to define a new ground truth similarity measure, which accurately reflects how different recommendation methods perform. A user study is conducted to evaluate both this measure, and the performance of integrated representations for recommending strong novel recommendations.
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Costa, Thales Filizola. "Taxonomy-driven content-based recommendation for new itens." Universidade Federal de Minas Gerais, 2014. http://hdl.handle.net/1843/ESBF-9KJR2D.
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Recommender systems aim at predicting the preference of a user towards a given item such as a movie, a song, or a news story. Effective recommendations can be produced through collaborative filtering, in which case the previously manifested preferences of a community of users are leveraged to inform the recommender system. Effective recommender systems must cope with an evolving item catalog and an increasing user base, leading to a considerable rate of new items and new users, both with unknown past preferences. This problem, known as the cold-start recommendation problem, may hamper the performance of recommender systems that are based solely on collaborative filtering. To overcome this problem, we propose an approach that exploits content-based features derived from taxonomies associated with the cataloged items. In contrast to previous content-based recommendation approaches, our approach is domain-agnostic, and can be directly deployed to produce effective cold-start recommendations in different domains. For domains where an explicit taxonomy is not available, we show that a suitable one can be derived implicitly using Latent Dirichlet Allocation. Our experiments using two publicly available datasets with distinct levels of sparsity attest the effectiveness of the proposed approach, which significantly outperforms several state-of-the-art baselines from the literature.
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Thollot, Raphaël. "Dynamic situation monitoring and Context-Aware BI recommendations." Phd thesis, Ecole Centrale Paris, 2012. http://tel.archives-ouvertes.fr/tel-00718917.
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The amount of information generated and maintained by information systems and their users leads to the increasingly important concern of information overload. Personalized systems have thus emerged to help provide more relevant information and services to the user. In particular, recommender systems appeared in the mid 1990's and have since then generated a growing interest in both industry and academia. Besides, context-aware systems have been developed to model, capture and interpret information about the user's situation, generally in dynamic and heterogeneous environments. Decision support systems like Business Intelligence (BI) platforms also face usability challenges as the amount of information available to knowledge workers grows. Remarkably, we observe that only a small part of personalization and recommendation techniques have been used in the context of data warehouses and analysis tools. Therefore, our work aims at exploring synergies of recommender systems and context-aware systems to develop personalization and recommendation scenarios suited in a BI environment. In response to this, we develop in our work an open and modular situation management platform using a graph-based situation model. Besides, dynamic aspects are crucial to deal with context data which is inherently time-dependent. We thus define two types of active components to enable dynamic maintenance of situation graphs, activation rules and operators. In response to events which can describe users' interactions, activation rules - defined using the event-condition-action framework - are evaluated thanks to queries on underlying graphs, to eventually trigger appropriate operators. These platform and framework allow us to develop and support various recommendation and personalization scenarios. Importantly, we design a re-usable personalized query expansion component, using semantics of multi-dimensional models and usage statistics from repositories of BI documents like reports or dashboards. This component is an important part of another experimentation we realized, Text-To-Query. This system dynamically generates multi-dimensional queries to illustrate a text and support the knowledge worker in the analysis or enrichment of documents she is manipulating. Besides, we also illustrate the integration and usage of our graph repository and situation management frameworks in an open and extensible federated search project, to provide background knowledge management and personalization.
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Guillou, Frédéric. "On recommendation systems in a sequential context." Thesis, Lille 3, 2016. http://www.theses.fr/2016LIL30041/document.
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Cette thèse porte sur l'étude des Systèmes de Recommandation dans un cadre séquentiel, où les retours des utilisateurs sur des articles arrivent dans le système l'un après l'autre. Après chaque retour utilisateur, le système doit le prendre en compte afin d'améliorer les recommandations futures. De nombreuses techniques de recommandation ou méthodologies d'évaluation ont été proposées par le passé pour les problèmes de recommandation. Malgré cela, l'évaluation séquentielle, qui est pourtant plus réaliste et se rapproche davantage du cadre d'évaluation d'un vrai système de recommandation, a été laissée de côté. Le contexte séquentiel nécessite de prendre en considération différents aspects non visibles dans un contexte fixe. Le premier de ces aspects est le dilemme dit d'exploration vs. exploitation: le modèle effectuant les recommandations doit trouver le bon compromis entre recueillir de l'information sur les goûts des utilisateurs à travers des étapes d'exploration, et exploiter la connaissance qu'il a à l'heure actuelle pour maximiser le feedback reçu. L'importance de ce premier point est mise en avant à travers une première évaluation, et nous proposons une approche à la fois simple et efficace, basée sur la Factorisation de Matrice et un algorithme de Bandit Manchot, pour produire des recommandations appropriées. Le second aspect pouvant apparaître dans le cadre séquentiel surgit dans le cas où une liste ordonnée d'articles est recommandée au lieu d'un seul article. Dans cette situation, le feedback donné par l'utilisateur est multiple: la partie explicite concerne la note donnée par l'utilisateur concernant l'article choisi, tandis que la partie implicite concerne les articles cliqués (ou non cliqués) parmi les articles de la liste. En intégrant les deux parties du feedback dans un modèle d'apprentissage, nous proposons une approche basée sur la Factorisation de Matrice, qui peut recommander de meilleures listes ordonnées d'articles, et nous évaluons cette approche dans un contexte séquentiel particulier pour montrer son efficacitéThis thesis is dedicated to the study of Recommendation Systems under a sequential setting, where the feedback given by users on items arrive one after another in the system. After each feedback, the system has to integrate it and try to improve future recommendations. Many techniques or evaluation methods have already been proposed to study the recommendation problem. Despite that, such sequential setting, which is more realistic and represent a closer framework to a real Recommendation System evaluation, has surprisingly been left aside. Under a sequential context, recommendation techniques need to take into consideration several aspects which are not visible for a fixed setting. The first one is the exploration-exploitation dilemma: the model making recommendations needs to find a good balance between gathering information about users' tastes or items through exploratory recommendation steps, and exploiting its current knowledge of the users and items to try to maximize the feedback received. We highlight the importance of this point through the first evaluation study and propose a simple yet efficient approach to make effective recommendation, based on Matrix Factorization and Multi-Armed Bandit algorithms. The second aspect emphasized by the sequential context appears when a list of items is recommended to the user instead of a single item. In such a case, the feedback given by the user includes two parts: the explicit feedback as the rating, but also the implicit feedback given by clicking (or not clicking) on other items of the list. By integrating both feedback into a Matrix Factorization model, we propose an approach which can suggest better ranked list of items, and we evaluate it in a particular setting
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Agagu, Tosin. "Recommendation Approaches Using Context-Aware Coupled Matrix Factorization." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/37012.
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In general, recommender systems attempt to estimate user preference based on historical data. A context-aware recommender system attempts to generate better recommendations using contextual information. However, generating recommendations for specific contexts has been challenging because of the difficulties in using contextual information to enhance the capabilities of recommender systems.
Several methods have been used to incorporate contextual information into traditional recommendation algorithms. These methods focus on incorporating contextual information to improve general recommendations for users rather than identifying the different context applicable to the user and providing recommendations geared towards those specific contexts.
In this thesis, we explore different context-aware recommendation techniques and present our context-aware coupled matrix factorization methods that use matrix factorization for estimating user preference and features in a specific contextual condition.
We develop two methods: the first method attaches user preference across multiple contextual conditions, making the assumption that user preference remains the same, but the suitability of items differs across different contextual conditions; i.e., an item might not be suitable for certain conditions. The second method assumes that item suitability remains the same across different contextual conditions but user preference changes. We perform a number of experiments on the last.fm dataset to evaluate our methods. We also compared our work to other context-aware recommendation approaches. Our results show that grouping ratings by context and jointly factorizing with common factors improves prediction accuracy.
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Lagerqvist, Gustaf, and Anton Stålhandske. "Recommendation systems for recruitment within an educational context." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-42902.
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Alongside the evolution of the recruitment process, different types of recommendation systems have been developed. The purpose of this study is to investigate recommendation systems within educational contexts, successful implementations of recommendation system architecture patterns, and alternatives to previous experience when evaluating candidates. The study is conducted through two separate methods; A literature review with a qualitative approach and design science research methodology focused on design and development, demonstration and evaluation. The literature review shows that, for recommendation systems, a layered architecture built within a microservice ecosystem is successfully utilized and has multiple beneficial aspects such as improved scalability, maintainability and security. Through design science research methodology, this study shows a suggested approach to implementing a layered architecture in combination with KNN and hybrid filtering. To avoid the lapse of suitable candidates, caused by demanding previous experience, this study shows an alternative approach to recruitment, within an educational context, through the use of soft skills. Within the study, this approach is successfully used to evaluate and compare students, but the same approach could possibly be applied to evaluate and compare companies. Moving forward, this study could be further expanded by looking into possible biases arising as a result of using AI and choices made during this study, as well as weighting of student-attributes.
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Akermi, Imen. "A hybrid model for context-aware proactive recommendation." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30101/document.
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L'accès aux informations pertinentes, adaptées aux besoins et au profil de l'utilisateur est un enjeu majeur dans le cadre actuel caractérisé par une prolifération massive des ressources d'information hétérogènes. Le développement d'appareils mobiles équipés de plusieurs fonctionnalités telles que la caméra, le WIFI, la géo-localisation et bien plus d'autres permettent aux systèmes mobiles de recommandation actuels d'être hautement contextualisés et pouvant fournir à l'utilisateur des informations pertinentes au bon moment quand il en a le plus besoin, sans attendre qu'il établisse une interaction avec son appareil. C'est dans ce cadre que s'insère notre travail de thèse. En effet, nous proposons une approche de recommandation contextuelle et proactive dans un environnement mobile qui permet de recommander des informations pertinentes à l'utilisateur sans attendre à ce que ce dernier initie une interaction. Un système proactif peut prendre la forme d'un guide touristique personnalisé qui se base sur la localisation et les préférences de l'utilisateur pour suggérer à ce dernier des endroits intéressants sans qu'il fournisse, sa préférence ou une requête explicite. Cela permettra de réduire les efforts, le temps et l'interaction de l'utilisateur avec son appareil mobile et de présenter les informations pertinentes au bon moment et au bon endroit. Cette approche prend aussi en considération les situations où la recommandation pourrait déranger l'utilisateur. Il s'agit d'équilibrer le processus de recommandation contre les interruptions intrusives. En effet, il existe différents facteurs et situations qui rendent l'utilisateur moins ouvert aux recommandations. Comme nous travaillons dans le contexte des appareils mobiles, nous considérons que les applications mobiles telles que la caméra, le clavier, l'agenda, etc., sont de bons représentants de l'interaction de l'utilisateur avec son appareil puisqu'ils représentent en quelque sorte la plupart des activités qu'un utilisateur pourrait entreprendre avec son appareil mobile au quotidien, comme envoyer des messages, converser, tweeter, naviguer ou prendre des photosJust-In-Time recommender systems involve all systems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large data space. The user does not need to formulate a query, this latter is implicit and corresponds to the resources that match the user's interests at the right time. Our work falls within this framework and focuses on developing a proactive context-aware recommendation approach for mobile devices that covers many domains. It aims at recommending relevant items that match users' personal interests at the right time without waiting for the users to initiate any interaction. Indeed, the development of mobile devices equipped with persistent data connections, geolocation, cameras and wireless capabilities allows current context-aware recommender systems (CARS) to be highly contextualized and proactive. We also take into consideration to which degree the recommendation might disturb the user. It is about balancing the process of recommendation against intrusive interruptions. As a matter of fact, there are different factors and situations that make the user less open to recommendations. As we are working within the context of mobile devices, we consider that mobile applications functionalities such as the camera, the keyboard, the agenda, etc., are good representatives of the user's interaction with his device since they somehow stand for most of the activities that a user could use in a mobile device in a daily basis such as texting messages, chatting, tweeting, browsing or taking selfies and pictures
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Strand, Anton, and Markus Gunnarsson. "Code Reviewer Recommendation : A Context-Aware Hybrid Approach." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18288.
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Background. Code reviewing is a commonly used practice in software development. It refers to the process of reviewing new code changes, commonly before they aremerged with the code base. However, in order to perform the review, developers need to be assigned to that task. The problems with a manual assignment includes a time-consuming selection process; limited pool of known candidates; risk of high reuse of the same reviewers (high workload). Objectives. This thesis aims to attempt to address the above issues with a recommendation system. The idea is to receive feedback from experienced developers in order to expand upon identified reviewer factors; which can be used to determinethe suitability of developers as reviewers for a given change. Also, to develop and implement a solution that uses some of the most promising reviewer factors. The solution can later be deployed and validated through user and reviewer feedback in a real large-scale project. The developed recommendation system is named Carrot. Methods. An improvement case study was conducted at Ericsson. The identification of reviewer factors is found through literature review and semi-structured interviews. Validation of Carrot’s usability was conducted through static analysis,user feedback, and static validation. Results. The results show that Carrot can help identify adequate non-obvious reviewers and be of great assistance to new developers. There are mixed opinions on Carrot’s ability to assist with workload balancing and decrease of review lead time. The recommendations can be performed in a production environment in less than a quarter of a second. Conclusions. The implemented and validated approach indicates possible usefulness in performing recommendations, but could benefit significantly from further improvements. Many of the problems seen with the recommendations seem to be a result of corner-cases that are not handled by the calculations. The problems would benefit considerably from further analysis and testing.
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Meguebli, Youssef. "Leveraging User-Generated Content for Enhancing and Personalizing News Recommendation." Thesis, CentraleSupélec, 2015. http://www.theses.fr/2015SUPL0007/document.
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La motivation principale de cette thèse est de proposer un système de recommandation personnalisé pour les plateformes d’informations. Pour cela, nous avons démontré que les opinions peuvent constituer un descripteur efficace pour améliorer la qualité de la recommandation. Au cours de cette thèse, nous avons abordé ce problème en proposant trois contributions principales. Tout d’abord, nous avons proposé un modèle de profil qui décrit avec précision les intérêts des utilisateurs ainsi que le contenu des articles de presse. Le modèle de profil proposé repose sur trois éléments : les entités nommées, les aspects et les sentiments. Nous avons testé notre modèle de profil sur les trois applications différentes que sont l’identification des orientations politiques des utilisateurs, la recommandation personnalisée des articles de presse et enfin la diversification de la liste des articles recommandés. Deuxièmement, nous avons proposé une approche de classement des opinions permettant de filtrer et sélectionner seulement les opinions pertinentes. Pour cela, nous avons utilisé une variation de la technique de PageRank pour définir le score de chaque opinion. Les résultats montrent que notre approche surpasse deux approches récemment proposées pour le classement des opinions. Troisièmement, nous avons étudié différentes façons d’enrichir le contenu des articles de presse par les opinions : par toutes les opinions, par seulement le topk des opinions, et enfin par un ensemble d’opinions diversifiées. Les résultats montrent que l’enrichissement des contenus des articles de presseIn this thesis, we have investigated how to exploit user-generated-content for personalized news recommendation purpose. The intuition behind this line of research is that the opinions provided by users, on news websites, represent a strong indicator about their profiles. We have addressed this problem by proposing three main contributions. Firstly, we have proposed a profile model that accurately describes both users’ interests and news article contents. The profile model was tested on three different applications ranging from identifying the political orientation of users to the context of news recommendation and the diversification of the list of recommended news articles. Results show that our profile model give much better results compared to state-of-the-art models. Secondly, we have investigated the problem of noise on opinions and how we can retrieve only relevant opinions in response to a given query.The proposed opinion ranking strategy is based on users’ debates features. We have used a variation of PageRank technique to define the score of each opinion. Results show that our approach outperforms two recent proposed opinions ranking strategies, particularly for controversial topics. Thirdly, we have investigated different ways of leveraging opinions on news article contents including all opinions, topk opinions based on opinion ranking strategy, and a set of diverse opinion. To extract a list of diverse opinions, we have employed a variation of an existing opinion diversification model. Results show that diverse opinions give the best performance over other leveraging strategies
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Wiklund, Ida. "A Recommendation system for News Push Notifications- Personalizing with a User-based and Content-based Recommendation system." Thesis, Umeå universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172275.
Full textAbstract:
The news landscape has changed during recent years because of the digitization. News can nowadays be found in both newspapers and on different sites online. The availability of the digital newspapers leads to competition among the news companies. To make the users stay on one specific platform for news, relevance is required in the content and oneway of creating relevance is through personalization, to tailor the content to each user. The focus of this thesis is therefore personalizing newspush notifications for a digital newspaper and making them more relevant for users. The project was made in cooperation with VK Media, and their digital newspaper. The task in this thesis is to implement personalization of push notifications by building a recommendation system and to test the implemented system with data from VK. In order to perform the task, a dataset representing reading habits of VK’s users was extracted from their data warehouse. Then a user-based and content-based recommendation system was implemented in Python.The idea with the system is to recommend new articles that are sufficiently similar to one or more of the already read articles. Articles that may be liked by one of the most similar users should also be recommended. Finally, the system’s performance was evaluated with the data representing reading habits for VK’s users. The results show that the implemented system has better performance than the current solution without any personalization, when recommending a few articles to each user. The results from the evaluation also show that the more articles the users have read, the better predictions are possible to make. Thus, this thesis offers a first step towards meeting the expectations of more relevant content among VK’s users.
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Song, Songbo. "Advanced personalization of IPTV services." Thesis, Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0001/document.
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Le monde de la TV est en cours de transformation de la télévision analogique à la télévision numérique, qui est capable de diffuser du contenu de haute qualité, offrir aux consommateurs davantage de choix, et rendre l'expérience de visualisation plus interactive. IPTV (Internet Protocol TV) présente une révolution dans la télévision numérique dans lequel les services de télévision numérique sont fournis aux utilisateurs en utilisant le protocole Internet (IP) au dessus d’une connexion haut débit. Les progrès de la technologie IPTV permettra donc un nouveau modèle de fourniture de services. Les fonctions offertes aux utilisateurs leur permettent de plus en plus d’autonomie et de plus en plus de choix. Il en est notamment ainsi de services de type ‘nTS’ (pour ‘network Time Shifting’ en anglais) qui permettent à un utilisateur de visionner un programme de télévision en décalage par rapport à sa programmation de diffusion, ou encore des services de type ‘nPVR’ (pour ‘network Personal Video Recorder’ en anglais) qui permettent d’enregistrer au niveau du réseau un contenu numérique pour un utilisateur. D'autre part, l'architecture IMS proposée dans NGN fournit une architecture commune pour les services IPTV. Malgré les progrès rapides de la technologie de télévision interactive (comprenant notamment les technologies IPTV et NGN), la personnalisation de services IPTV en est encore à ses débuts. De nos jours, la personnalisation des services IPTV se limite principalement à la recommandation de contenus et à la publicité ciblée. Ces services ne sont donc pas complètement centrés sur l’utilisateur, alors que choisir manuellement les canaux de diffusion et les publicités désirées peut représenter une gêne pour l’utilisateur. L’adaptation des contenus numériques en fonction de la capacité des réseaux et des dispositifs utilisés n’est pas encore prise en compte dans les implémentations actuelles. Avec le développement des technologies numériques, les utilisateurs sont amenés à regarder la télévision non seulement sur des postes de télévision, mais également sur des smart phones, des tablettes digitales, ou encore des PCs. En conséquence, personnaliser les contenus IPTV en fonction de l’appareil utilisé pour regarder la télévision, en fonction des capacités du réseau et du contexte de l’utilisateur représente un défi important. Cette thèse présente des solutions visant à améliorer la personnalisation de services IPTV à partir de trois aspects: 1) Nouvelle identification et authentification pour services IPTV. 2) Nouvelle architecture IPTV intégrée et comportant un système de sensibilité au contexte pour le service de personnalisation. 3) Nouveau service de recommandation de contenu en fonction des préférences de l’utilisateur et aussi des informations contextesInternet Protocol TV (IPTV) delivers television content to users over IP-based network. Different from the traditional TV services, IPTV platforms provide users with large amount of multimedia contents with interactive and personalized services, including the targeted advertisement, on-demand content, personal video recorder, and so on. IPTV is promising since it allows to satisfy users experience and presents advanced entertainment services. On the other hand, the Next Generation Network (NGN) approach in allowing services convergence (through for instance coupling IPTV with the IP Multimedia Subsystem (IMS) architecture or NGN Non-IMS architecture) enhances users’ experience and allows for more services personalization. Although the rapid advancement in interactive TV technology (including IPTV and NGN technologies), services personalization is still in its infancy, lacking the real distinguish of each user in a unique manner, the consideration of the context of the user (who is this user, what is his preferences, his regional area, location, ..) and his environment (characteristics of the users’ devices ‘screen types, size, supported resolution, ‘‘ and networks available network types to be used by the user, available bandwidth, ..’) as well as the context of the service itself (content type and description, available format ‘HD/SD’, available language, ..) in order to provide the adequate personalized content for each user. This advanced IPTV services allows services providers to promote new services and open new business opportunities and allows network operators to make better utilization of network resources through adapting the delivered content according to the available bandwidth and to better meet the QoE (Quality of Experience) of clients. This thesis focuses on enhanced personalization for IPTV services following a user-centric context-aware approach through providing solutions for: i) Users’ identification during IPTV service access through a unique and fine-grained manner (different from the identification of the subscription which is the usual current case) based on employing a personal identifier for each user which is a part of the user context information. ii) Context-Aware IPTV service through proposing a context-aware system on top of the IPTV architecture for gathering in a dynamic and real-time manner the different context information related to the user, devices, network and service. The context information is gathered throughout the whole IPTV delivery chain considering the user domain, network provider domain, and service/content provider domain. The proposed context-aware system allows monitoring user’s environment (devices and networks status), interpreting user’s requirements and making the user’s interaction with the TV system dynamic and transparent. iii) Personalized recommendation and selection of IPTV content based on the different context information gathered and the personalization decision taken by the context-aware system (different from the current recommendation approach mainly based on matching content to users’ preferences) which in turn highly improves the users’ Quality of Experience (QoE) and enriching the offers of IPTV services
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Alhamid, Mohammed F. "Towards Context-Aware Personalized Recommendations in an Ambient Intelligence Environment." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32052.
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Due to the rapid increase of social network resources and services, Internet users are now overwhelmed by the vast quantity of social media available. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is still a need to reinforce the recommendation process in a systematic way, with context-adaptive information. This thesis proposes a recommendation model, called HPEM, that establishes a bridge between the multimedia resources, user collaborative preferences, and the detected contextual information, including physiological parameters. The collection of contextual information and the delivery of the resulted recommendation is made possible by adapting the user’s environment using Ambient Intelligent (AmI) interfaces. Additionally, this thesis presents the potential of including a user’s biological signal and leveraging it within an adapted collaborative filtering algorithm in the recommendation process. First, the different versions of the proposed HPEM model utilize existing online social networks by incorporating social tags and rating information in ways that personalize the search for content in a particular detected context. By leveraging the social tagging, our proposed model computes the hidden preferences of users in certain contexts from other similar contexts, as well as the hidden assignment of contexts for items from other similar items. Second, we demonstrate the use of an optimization function to maximize the Mean Average
Prevision (MAP) measure of the resulted recommendations.
We demonstrate the feasibility of HPEM with two prototype applications that use
contextual information for recommendations. Offline and online experiments have been conducted to measure the accuracy of delivering personalized recommendations, based on the user’s context; two real-world and one collected semi-synthetic datasets were used. Our evaluation results show a potential improvement to the quality of the recommendation when compared to state-of-the-art recommendation algorithms that consider contextual information. We also compare the proposed method to other algorithms, where user’s context is not used to personalize the recommendation results. Additionally, the results obtained demonstrate certain improvements on cold start situations, where relatively little information is known about a user or an item.
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Nguyen, Ngoc Chan. "Service recommendation for individual and process use." Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00789726.
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Web services have been developed as an attractive paradigm for publishing, discovering and consuming services. They are loosely-coupled applications that can be run alone or be composed to create new value-added services. They can be consumed as individual services which provide a unique interface to receive inputs and return outputs; or they can be consumed as components to be integrated into business processes. We call the first consumption case individual use and the second case business process use. The requirement of specific tools to assist consumers in the two service consumption cases involves many researches in both academics and industry. On the one hand, many service portals and service crawlers have been developed as specific tools to assist users to search and invoke Web services for individual use. However, current approaches take mainly into account explicit knowledge presented by service descriptions. They make recommendations without considering data that reflect user interest and may require additional information from users. On the other hand, some business process mechanisms to search for similar business process models or to use reference models have been developed. These mechanisms are used to assist process analysts to facilitate business process design. However, they are labor-intense, error-prone, time-consuming, and may make business analyst confused. In our work, we aim at facilitating the service consumption for individual use and business process use using recommendation techniques. We target to recommend users services that are close to their interest and to recommend business analysts services that are relevant to an ongoing designed business process. To recommend services for individual use, we take into account the user's usage data which reflect the user's interest. We apply well-known collaborative filtering techniques which are developed for making recommendations. We propose five algorithms and develop a web-based application that allows users to use services. To recommend services for business process use, we take into account the relations between services in business processes. We target to recommend relevant services to selected positions in a business process. We define the neighborhood context of a service. We make recommendations based on the neighborhood context matching. Besides, we develop a query language to allow business analysts to formally express constraints to filter services. We also propose an approach to extract the service's neighborhood context from business process logs. Finally, we develop three applications to validate our approach. We perform experiments on the data collected by our applications and on two large public datasets. Experimental results show that our approach is feasible, accurate and has good performance in real use-cases
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Al-Ghossein, Marie. "Context-aware recommender systems for real-world applications." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT008/document.
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Les systèmes de recommandation se sont révélés être des outils efficaces pour aider les utilisateurs à faire face à la surcharge informationnelle. D’importants progrès ont été réalisés dans le domaine durant les deux dernières décennies, menant en particulier à l’exploitation de l’information contextuelle pour modéliser l’aspect dynamique des utilisateurs et des articles. La définition traditionnelle du contexte, adoptée dans la plupart des systèmes de recommandation contextuels, ne répond pas à plusieurs contraintes rencontrées dans les applications du monde réel. Dans cette thèse, nous abordons les problèmes de recommandation en présence d’informations contextuelles partiellement observables et d’informations contextuelles non observables dans deux applications particulières, la recommandation d’hôtels et la recommandation en ligne, remettant en question plusieurs aspects de la définition traditionnelle du contexte, notamment l'accessibilité, la pertinence, l'acquisition et la modélisation.La première partie de la thèse étudie le problème de recommandation d’hôtels qui souffre du démarrage à froid continu, limitant la performance des approches classiques de recommandation. Le voyage n’est pas une activité fréquente et les utilisateurs ont tendance à adopter des comportements diversifiés en fonction de leurs situations spécifiques. Après une analyse du comportement des utilisateurs dans ce domaine, nous proposons de nouvelles approches de recommandation intégrant des informations contextuelles partiellement observables affectant les utilisateurs. Nous montrons comment cela contribue à améliorer la qualité des recommandations.La deuxième partie de la thèse aborde le problème de recommandation en ligne en présence de flux de données où les observations apparaissent continûment à haute fréquence. Nous considérons que les utilisateurs et les articles reposent sur des informations contextuelles non observables par le système et évoluent de façons différentes à des rythmes différents. Nous proposons alors d’effectuer de la détection active de changements et d’assurer la mise à jour des modèles en temps réel. Nous concevons de nouvelles méthodes qui s’adaptent aux changements qui apparaissent au niveau des préférences des utilisateurs et des perceptions et descriptions des articles, et montrons l’importance de la recommandation adaptative en ligne pour garantir de bonnes performances au cours du tempsRecommender systems have proven to be valuable tools to help users overcome the information overload, and significant advances have been made in the field over the last two decades. In particular, contextual information has been leveraged to model the dynamics occurring within users and items. Context is a complex notion and its traditional definition, which is adopted in most recommender systems, fails to cope with several issues occurring in real-world applications. In this thesis, we address the problems of partially observable and unobservable contexts in two particular applications, hotel recommendation and online recommendation, challenging several aspects of the traditional definition of context, including accessibility, relevance, acquisition, and modeling.The first part of the thesis investigates the problem of hotel recommendation which suffers from the continuous cold-start issue, limiting the performance of classical approaches for recommendation. Traveling is not a frequent activity and users tend to have multifaceted behaviors depending on their specific situation. Following an analysis of the user behavior in this domain, we propose novel recommendation approaches integrating partially observable context affecting users and we show how it contributes in improving the recommendation quality.The second part of the thesis addresses the problem of online adaptive recommendation in streaming environments where data is continuously generated. Users and items may depend on some unobservable context and can evolve in different ways and at different rates. We propose to perform online recommendation by actively detecting drifts and updating models accordingly in real-time. We design novel methods adapting to changes occurring in user preferences, item perceptions, and item descriptions, and show the importance of online adaptive recommendation to ensure a good performance over time
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McParlane, Philip. "The role of context in image annotation and recommendation." Thesis, University of Glasgow, 2016. http://theses.gla.ac.uk/7676/.
Full textAbstract:
With the rise of smart phones, lifelogging devices (e.g. Google Glass) and popularity of image sharing websites (e.g. Flickr), users are capturing and sharing every aspect of their life online producing a wealth of visual content. Of these uploaded images, the majority are poorly annotated or exist in complete semantic isolation making the process of building retrieval systems difficult as one must firstly understand the meaning of an image in order to retrieve it. To alleviate this problem, many image sharing websites offer manual annotation tools which allow the user to “tag” their photos, however, these techniques are laborious and as a result have been poorly adopted; Sigurbjörnsson and van Zwol (2008) showed that 64% of images uploaded to Flickr are annotated with < 4 tags. Due to this, an entire body of research has focused on the automatic annotation of images (Hanbury, 2008; Smeulders et al., 2000; Zhang et al., 2012a) where one attempts to bridge the semantic gap between an image’s appearance and meaning e.g. the objects present. Despite two decades of research the semantic gap still largely exists and as a result automatic annotation models often offer unsatisfactory performance for industrial implementation. Further, these techniques can only annotate what they see, thus ignoring the “bigger picture” surrounding an image (e.g. its location, the event, the people present etc). Much work has therefore focused on building photo tag recommendation (PTR) methods which aid the user in the annotation process by suggesting tags related to those already present. These works have mainly focused on computing relationships between tags based on historical images e.g. that NY and timessquare co-exist in many images and are therefore highly correlated. However, tags are inherently noisy, sparse and ill-defined often resulting in poor PTR accuracy e.g. does NY refer to New York or New Year? This thesis proposes the exploitation of an image’s context which, unlike textual evidences, is always present, in order to alleviate this ambiguity in the tag recommendation process. Specifically we exploit the “what, who, where, when and how” of the image capture process in order to complement textual evidences in various photo tag recommendation and retrieval scenarios. In part II, we combine text, content-based (e.g. # of faces present) and contextual (e.g. day-of-the-week taken) signals for tag recommendation purposes, achieving up to a 75% improvement to precision@5 in comparison to a text-only TF-IDF baseline. We then consider external knowledge sources (i.e. Wikipedia & Twitter) as an alternative to (slower moving) Flickr in order to build recommendation models on, showing that similar accuracy could be achieved on these faster moving, yet entirely textual, datasets. In part II, we also highlight the merits of diversifying tag recommendation lists before discussing at length various problems with existing automatic image annotation and photo tag recommendation evaluation collections. In part III, we propose three new image retrieval scenarios, namely “visual event summarisation”, “image popularity prediction” and “lifelog summarisation”. In the first scenario, we attempt to produce a rank of relevant and diverse images for various news events by (i) removing irrelevant images such memes and visual duplicates (ii) before semantically clustering images based on the tweets in which they were originally posted. Using this approach, we were able to achieve over 50% precision for images in the top 5 ranks. In the second retrieval scenario, we show that by combining contextual and content-based features from images, we are able to predict if it will become “popular” (or not) with 74% accuracy, using an SVM classifier. Finally, in chapter 9 we employ blur detection and perceptual-hash clustering in order to remove noisy images from lifelogs, before combining visual and geo-temporal signals in order to capture a user’s “key moments” within their day. We believe that the results of this thesis show an important step towards building effective image retrieval models when there lacks sufficient textual content (i.e. a cold start).
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Nelaturu, Keerthi. "Content Management and Hashtag Recommendation in a P2P Social Networking Application." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32501.
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In this thesis focus is on developing an online social network application with a Peer-to-Peer infrastructure motivated by BestPeer++ architecture and BATON overlay structure. BestPeer++ is a data processing platform which enables data sharing between enterprise systems. BATON is an open-sourced project which implements a peer-to-peer with a topology of a balanced tree.
We designed and developed the components for users to manage their accounts, maintain friend relationships, and publish their contents with privacy control and newsfeed, notification requests in this social network- ing application.
We also developed a Hashtag Recommendation system for this social net- working application. A user may invoke a recommendation procedure while writing a content. After being invoked, the recommendation pro- cedure returns a list of candidate hashtags, and the user may select one hashtag from the list and embed it into the content. The proposed ap- proach uses Latent Dirichlet Allocation (LDA) topic model to derive the latent or hidden topics of different content. LDA topic model is a well developed data mining algorithm and generally effective in analyzing text documents with different lengths. The topic model is further used to identify the candidate hashtags that are associated with the texts in the published content through their association with the derived hidden top- ics.
We considered different methods of recommendation approach for the pro- cedure to select candidate hashtags from different content. Some methods consider the hashtags contained in the contents of the whole social net- work or of the user self. These are content-based recommendation tech- niques which matching user’s own profile with the profiles of items.. Some methods consider the hashtags contained in contents of the friends or of the similar users. These are collaborative filtering based recommendation
techniques which considers the profiles of other users in the system. At the end of the recommendation procedure, the candidate hashtags are or- dered by their probabilities of appearance in the content and returned to the user.
We also conducted experiments to evaluate the effectiveness of the hashtag recommendation approach. These experiments were fed with the tweets published in Twitter. The hit-rate of recommendation is measured in these experiments. Hit-rate is the percentage of the selected or relevant hashtags contained in candidate hashtags. Our experiment results show that the hit-rate above 50% is observed when we use a method of recommendation approach independently. Also, for the case that both similar user and user preferences are considered at the same time, the hit-rate improved to 87% and 92% for top-5 and top-10 candidate recommendations respectively.
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Landia, Nikolas. "Content-awareness and graph-based ranking for tag recommendation in folksonomies." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/58069/.
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Tag recommendation algorithms aid the social tagging process in many userdriven document indexing applications, such as social bookmarking and publication sharing websites. This thesis gives an overview of existing tag recommendation methods and proposes novel approaches that address the new document problem and the task of ranking tags. The focus is on graph-based methods such as Folk- Rank that apply weight spreading algorithms to a graph representation of the folksonomy. In order to suggest tags for previously untagged documents, extensions are presented that introduce content into the recommendation process as an additional information source. To address the problem of ranking tags, an in-depth analysis of graph models as well as ranking algorithms is conducted. Implicit assumptions made by the widely-used graph model of the folksonomy are highlighted and an improved model is proposed that captures the characteristics of the social tagging data more accurately. Additionally, issues in the tag rank computation of FolkRank are analysed and an adapted weight spreading approach for social tagging data is presented. Moreover, the applicability of conventional weight spreading methods to data from the social tagging domain is examined in detail. Finally, indications of implicit negative feedback in the data structure of folksonomies are analysed and novel approaches of identifying negative relationships are presented. By exploiting the three-dimensional characteristics of social tagging data the proposed metrics are based on stronger evidence and provide reliable measures of negative feedback. Including content into the tag recommendation process leads to a significant increase in recommendation accuracy on real-world datasets. The proposed adaptations to graph models and ranking algorithms result in more accurate and computationally less expensive recommenders. Moreover, new insights into the fundamental characteristics of social tagging data are revealed and a novel data interpretation that takes negative feedback into account is proposed.
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Somasundaram, Jyothilakshmi. "Releasing Recommendation Datasets while Preserving Privacy." Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1306427987.
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Lotz-Sisitka, Heila 1965. "Viewpoint: reading conference recommendations in a wider context of social change." Environmental Education Association of Southern Africa, 2008. http://hdl.handle.net/10962/67411.
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publisher versionThis short Viewpoint paper considers the role and value of conference recommendations in shaping the field of environmental education. It explores the social politics, and often contested nature, of conference recommendations and their institutional histories, arguing that the act of producing conference recommendations forms part of the practices of new social movements. The paper recommends historicising conference recommendations and OEcross readings‚ to consider changing discourses and new developments in the field. Accompanying the short Viewpoint paper, are two sets of recently produced conference recommendations, one from the 4th International Environmental Education Conference held in Ahmedabad, India, and the other from the 1st International Conference on Mainstreaming Environment and Sustainability in African Universities held in Nairobi, Kenya.
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Deirmenci, Hazim. "Enabling Content Discovery in an IPTV System : Using Data from Online Social Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200922.
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Internet Protocol television (IPTV) is a way of delivering television over the Internet, which enables two-way communication between an operator and its users. By using IPTV, users have freedom to choose what content they want to consume and when they want to consume it. For example, users are able to watch TV shows after they have been aired on TV, and they can access content that is not part of any linear TV broadcasts, e.g. movies that are available to rent. This means that, by using IPTV, users can get access to more video content than is possible with the traditional TV distribution formats. However, having more options also means that deciding what to watch becomes more difficult, and it is important that IPTV providers facilitate the process of finding interesting content so that the users find value in using their services. In this thesis, the author investigated how a user’s online social network can be used as a basis for facilitating the discovery of interesting movies in an IPTV environment. The study consisted of two parts, a theoretical and a practical. In the theoretical part, a literature study was carried out in order to obtain knowledge about different recommender system strategies. In addition to the literature study, a number of online social network platforms were identified and empirically studied in order to gain knowledge about what data is possible to gather from them, and how the data can be gathered. In the practical part, a prototype content discovery system, which made use of the gathered data, was designed and built. This was done in order to uncover difficulties that exist with implementing such a system. The study shows that, while it is is possible to gather data from different online social networks, not all of them offer data in a form that is easy to make use of in a content discovery system. Out of the investigated online social networks, Facebook was found to offer data that is the easiest to gather and make use of. The biggest obstacle, from a technical point of view, was found to be the matching of movie titles gathered from the online social network with the movie titles in the database of the IPTV service provider; one reason for this is that movies can have titles in different languages.Internet Protocol television (IPTV) är ett sätt att leverera tv via Internet, vilket möjliggör tvåvägskommunikation mellan en operatör och dess användare. Genom att använda IPTV har användare friheten att välja vilket innehåll de vill konsumera och när de vill konsumera det. Användare har t.ex. möjlighet att titta på tv program efter att de har sänts på tv, och de kan komma åt innehåll som inte är en del av någon linjär tv-sändning, t.ex. filmer som är tillgängliga att hyra. Detta betyder att användare, genom att använda IPTV, kan få tillgång till mer videoinnhåll än vad som är möjligt med traditionella tv-distributionsformat. Att ha fler valmöjligheter innebär dock även att det blir svårare att bestämma sig för vad man ska titta på, och det är viktigt att IPTV-leverantörer underlättar processen att hitta intressant innehåll så att användarna finner värde i att använda deras tjänster. I detta exjobb undersökte författaren hur en användares sociala nätverk på Internet kan användas som grund för att underlätta upptäckandet av intressanta filmer i en IPTV miljö. Undersökningen bestod av två delar, en teoretisk och en praktisk. I den teoretiska delen genomfördes en litteraturstudie för att få kunskap om olika rekommendationssystemsstrategier. Utöver litteraturstudien identifierades ett antal sociala nätverk på Internet som studerades empiriskt för att få kunskap om vilken data som är möjlig att hämta in från dem och hur datan kan inhämtas. I den praktiska delen utformades och byggdes en prototyp av ett s.k. content discovery system (“system för att upptäcka innehåll”), som använde sig av den insamlade datan. Detta gjordes för att exponera svårigheter som finns med att implementera ett sådant system. Studien visar att, även om det är möjligt att samla in data från olika sociala nätverk på Internet så erbjuder inte alla data i en form som är lätt att använda i ett content discovery system. Av de undersökta sociala nätverkstjänsterna visade det sig att Facebook erbjuder data som är lättast att samla in och använda. Det största hindret, ur ett tekniskt perspektiv, visade sig vara matchningen av filmtitlar som inhämtats från den sociala nätverkstjänsten med filmtitlarna i IPTV-leverantörens databas; en anledning till detta är att filmer kan ha titlar på olika språk.
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Yeung, Kam Fung. "A context-aware framework for personalised recommendation in mobile environments." Thesis, University of Portsmouth, 2011. https://researchportal.port.ac.uk/portal/en/theses/a-contextaware-framework-for-personalised-recommendation-in-mobile-environments(c271b461-3f05-456f-a8f0-1996a9ad1549).html.
Full textAbstract:
Context-awareness has become an essential part in various personalised applications such as mobile recommeder systems and mobile information retrieval. Much progress has been made in context-aware applications. However there is a lack of general framework for supporting the rapid development of context-aware applications and enabling the sharing and dissemiation of context information across different applications
41
Bambia, Meriam. "Jointly integrating current context and social influence for improving recommendation." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30110/document.
Full textAbstract:
La diversité des contenus recommandation et la variation des contextes des utilisateurs rendent la prédiction en temps réel des préférences des utilisateurs de plus en plus difficile mettre en place. Toutefois, la plupart des approches existantes n'utilisent que le temps et l'emplacement actuels séparément et ignorent d'autres informations contextuelles sur lesquelles dépendent incontestablement les préférences des utilisateurs (par exemple, la météo, l'occasion). En outre, ils ne parviennent pas considérer conjointement ces informations contextuelles avec les interactions sociales entre les utilisateurs. D'autre part, la résolution de problèmes classiques de recommandation (par exemple, aucun programme de télévision vu par un nouvel utilisateur connu sous le nom du problème de démarrage froid et pas assez d'items co-évalués par d'autres utilisateurs ayant des préférences similaires, connu sous le nom du problème de manque de donnes) est d'importance significative puisque sont attaqués par plusieurs travaux. Dans notre travail de thèse, nous proposons un modèle probabiliste qui permet exploiter conjointement les informations contextuelles actuelles et l'influence sociale afin d'améliorer la recommandation des items. En particulier, le modèle probabiliste vise prédire la pertinence de contenu pour un utilisateur en fonction de son contexte actuel et de son influence sociale. Nous avons considérer plusieurs éléments du contexte actuel des utilisateurs tels que l'occasion, le jour de la semaine, la localisation et la météo. Nous avons utilisé la technique de lissage Laplace afin d'éviter les fortes probabilités. D'autre part, nous supposons que l'information provenant des relations sociales a une influence potentielle sur les préférences des utilisateurs. Ainsi, nous supposons que l'influence sociale dépend non seulement des évaluations des amis mais aussi de la similarité sociale entre les utilisateurs. Les similarités sociales utilisateur-ami peuvent être établies en fonction des interactions sociales entre les utilisateurs et leurs amis (par exemple les recommandations, les tags, les commentaires). Nous proposons alors de prendre en compte l'influence sociale en fonction de la mesure de similarité utilisateur-ami afin d'estimer les préférences des utilisateurs. Nous avons mené une série d'expérimentations en utilisant un ensemble de donnes réelles issues de la plateforme de TV sociale Pinhole. Cet ensemble de donnes inclut les historiques d'accès des utilisateurs-vidéos et les réseaux sociaux des téléspectateurs. En outre, nous collectons des informations contextuelles pour chaque historique d'accès utilisateur-vidéo saisi par le système de formulaire plat. Le système de la plateforme capture et enregistre les dernières informations contextuelles auxquelles le spectateur est confronté en regardant une telle vidéo.Dans notre évaluation, nous adoptons le filtrage collaboratif axé sur le temps, le profil dépendant du temps et la factorisation de la matrice axe sur le réseau social comme tant des modèles de référence. L'évaluation a port sur deux tâches de recommandation. La première consiste sélectionner une liste trie de vidéos. La seconde est la tâche de prédiction de la cote vidéo. Nous avons évalué l'impact de chaque élément du contexte de visualisation dans la performance de prédiction. Nous testons ainsi la capacité de notre modèle résoudre le problème de manque de données et le problème de recommandation de démarrage froid du téléspectateur. Les résultats expérimentaux démontrent que notre modèle surpasse les approches de l'état de l'art fondes sur le facteur temps et sur les réseaux sociaux. Dans les tests des problèmes de manque de donnes et de démarrage froid, notre modèle renvoie des prédictions cohérentes différentes valeurs de manque de donnéesDue to the diversity of alternative contents to choose and the change of users' preferences, real-time prediction of users' preferences in certain users' circumstances becomes increasingly hard for recommender systems. However, most existing context-aware approaches use only current time and location separately, and ignore other contextual information on which users' preferences may undoubtedly depend (e.g. weather, occasion). Furthermore, they fail to jointly consider these contextual information with social interactions between users. On the other hand, solving classic recommender problems (e.g. no seen items by a new user known as cold start problem, and no enough co-rated items with other users with similar preference as sparsity problem) is of significance importance since it is drawn by several works. In our thesis work, we propose a context-based approach that leverages jointly current contextual information and social influence in order to improve items recommendation. In particular, we propose a probabilistic model that aims to predict the relevance of items in respect with the user's current context. We considered several current context elements such as time, location, occasion, week day, location and weather. In order to avoid strong probabilities which leads to sparsity problem, we used Laplace smoothing technique. On the other hand, we argue that information from social relationships has potential influence on users' preferences. Thus, we assume that social influence depends not only on friends' ratings but also on social similarity between users. We proposed a social-based model that estimates the relevance of an item in respect with the social influence around the user on the relevance of this item. The user-friend social similarity information may be established based on social interactions between users and their friends (e.g. recommendations, tags, comments). Therefore, we argue that social similarity could be integrated using a similarity measure. Social influence is then jointly integrated based on user-friend similarity measure in order to estimate users' preferences. We conducted a comprehensive effectiveness evaluation on real dataset crawled from Pinhole social TV platform. This dataset includes viewer-video accessing history and viewers' friendship networks. In addition, we collected contextual information for each viewer-video accessing history captured by the plat form system. The platform system captures and records the last contextual information to which the viewer is faced while watching such a video. In our evaluation, we adopt Time-aware Collaborative Filtering, Time-Dependent Profile and Social Network-aware Matrix Factorization as baseline models. The evaluation focused on two recommendation tasks. The first one is the video list recommendation task and the second one is video rating prediction task. We evaluated the impact of each viewing context element in prediction performance. We tested the ability of our model to solve data sparsity and viewer cold start recommendation problems. The experimental results highlighted the effectiveness of our model compared to the considered baselines. Experimental results demonstrate that our approach outperforms time-aware and social network-based approaches. In the sparsity and cold start tests, our approach returns consistently accurate predictions at different values of data sparsity
42
Zanarella, Leonardo. "Progettazione ed Implementazione di Recommendation Content-based Filtering basato su Apache Mahout." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
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Karaman, Hilal. "A Content Based Movie Recommendation System Empowered By Collaborative Missing Data Prediction." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612037/index.pdf.
Full textAbstract:
The evolution of the Internet has brought us into a world that represents a huge amount of information items such as music, movies, books, web pages, etc. with varying quality. As a result of this huge universe of items, people get confused and the question &ldquoWhich one should I choose?&rdquo
arises in their minds. Recommendation Systems address the problem of getting confused about items to choose, and filter a specific type of information with a specific information filtering technique that attempts to present information items that are likely of interest to the user. A variety of information filtering techniques have been proposed for performing recommendations, including content-based and collaborative techniques which are the most commonly used approaches in recommendation systems. This thesis work introduces ReMovender, a content-based movie recommendation system which is empowered by collaborative missing data prediction. The distinctive point of this study lies in the methodology used to correlate the users in the system with one another and the usage of the content information of movies. ReMovender makes it possible for the users to rate movies in a scale from one to five. By using these ratings, it finds similarities among the users in a collaborative manner to predict the missing ratings data. As for the content-based part, a set of movie features are used in order to correlate the movies and produce recommendations for the users.
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Mourao, Fernando Henrique de Jesus. "A hybrid recommendation method that combines forgotten items and non-content attributes." Universidade Federal de Minas Gerais, 2014. http://hdl.handle.net/1843/ESBF-9TELK3.
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Recommender Systems (RSs) play important role in many Web applications nowadays, helping users to find their favorite items amid a huge number of options. Among numerous open challenges inherent to RSs, this dissertation addressed the challenge of enhancing the discovery of potentially relevant items for each user. In this sense, we exploited two algorithmic limitations unaddressed in the literature. First, RSs fail to bring back items consumed long ago that are potentially relevant for users nowadays. Second, RSs fail to capture the whole extent on which implicit signals of preferences observed on past consumption relate to preferences observed on current consumption. We addressed the first limitation by reviewing the users long-term history and identifying a subset of consumed items forgotten but still re-consumable (i.e., forgotten re-consumable items). We mitigated the second limitation by explicitly modeling the subset of attributes derived from metadata or consumption data (i.e., non-content attributes). Finally, we proposed ForNonContent, a hybrid method that addresses both limitations simultaneously. Besides validating these algorithmic limitations, offline analysis on four real datasets demonstrated that recommending forgotten re-consumable items may bring diversified and novel recommendations. Also, we found that non-content attributes may enhance ecommendations of six major RSs. Furthermore, we identified a complementary nature of the enhancements associated to each limitation. Finally, a user study with MovieLens users demonstrated that they valued more the recommendations issued by ForNonContent. In summary, this work pointed out a new and promising direction to enhance the user experience with RSs.Sistemas de Recomendação (SsR) desempenham um papel importante em diversas aplicações Web, ajudando os usuários a encontrar seus itens favoritos em meio a um grande número de opções. Dentre os vários desafios ainda em aberto inerentes a SsR, esta tese aborda o desafio de ampliar a descoberta de itens potencialmente relevantes para cada usuário. Neste sentido, exploramos duas limitações algorítmicas despercebidas na literatura. Primeiro, SsR falham em recuperar itens consumidos há muito tempo que são potencialmente relevantes para os usuários atualmente. Em segundo lugar, SsR não conseguem capturar toda a extensão na qual sinais implícitos de preferências observadas no consumo passado se relacionam com preferências observadas no consumo atual. Abordamos a primeira limitação revisando o passado remoto de consumo de cada usuário e identificando um subconjunto de itens consumidos e esquecidos atualmente, mas ainda re-consumíveis (i.e., itens esquecidos re-consumíveis). Mitigamos a segunda limitação modelando explicitamente um subconjunto de atributos derivados de dados de consumo e metadados (i.e., atributos não baseados em conteúdo). Finalmente, propusemos ForNonContent, um método híbrido que aborda ambas limitações simultaneamente. Além de validar a existência de tais limitações lgorítmicas, análises offline em quatro conjuntos de dados reais demonstraram que recomendar itens esquecidos re-consumíveis pode propiciar recomendações diversificadas e não óbvias. Verificamos também que os atributos não baseados em conteúdo podem aperfeiçoar recomendações geradas por seis principais SsR. Ademais, identificamos uma natureza complementar entre as melhorias associadas a cada limitação. Finalmente, avaliações com usuários reais do sistema MovieLens demonstraram que usuários apreciaram as recomendações geradas por ForNonContent. Em suma, este trabalho apontou uma nova e promissora direção para melhorar a experiência dos usuários com SsR.
45
Lokesh, Ashwini. "A Comparative Study of Recommendation Systems." TopSCHOLAR®, 2019. https://digitalcommons.wku.edu/theses/3166.
Full textAbstract:
Recommendation Systems or Recommender Systems have become widely popular due to surge of information at present time and consumer centric environment. Researchers have looked into a wide range of recommendation systems leveraging a wide range of algorithms. This study investigates three popular recommendation systems in existence, Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation system. The famous MovieLens dataset was utilized for the purpose of this study. The evaluation looked into both quantitative and qualitative aspects of the recommendation systems. We found that from both the perspectives, the hybrid recommendation system performs comparatively better than standalone Collaborative Filtering or Content-Based Filtering recommendation system
46
Dong, Qin. "Research on MNCs' Supply Chain Implementation in China. Contents, problems and Recommendations." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00601747.
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Désoyer, Adèle. "Appariement de contenus textuels dans le domaine de la presse en ligne : développement et adaptation d'un système de recherche d'information." Thesis, Paris 10, 2017. http://www.theses.fr/2017PA100119/document.
Full textAbstract:
L'objectif de cette thèse, menée dans un cadre industriel, est d'apparier des contenus textuels médiatiques. Plus précisément, il s'agit d'apparier à des articles de presse en ligne des vidéos pertinentes, pour lesquelles nous disposons d'une description textuelle. Notre problématique relève donc exclusivement de l'analyse de matériaux textuels, et ne fait intervenir aucune analyse d'image ni de langue orale. Surviennent alors des questions relatives à la façon de comparer des objets textuels, ainsi qu'aux critères mobilisés pour estimer leur degré de similarité. L'un de ces éléments est selon nous la similarité thématique de leurs contenus, autrement dit le fait que deux documents doivent relater le même sujet pour former une paire pertinente. Ces problématiques relèvent du domaine de la recherche d'information (ri), dans lequel nous nous ancrons principalement. Par ailleurs, lorsque l'on traite des contenus d'actualité, la dimension temporelle est aussi primordiale et les problématiques qui l'entourent relèvent de travaux ayant trait au domaine du topic detection and tracking (tdt) dans lequel nous nous inscrivons également.Le système d'appariement développé dans cette thèse distingue donc différentes étapes qui se complètent. Dans un premier temps, l'indexation des contenus fait appel à des méthodes de traitement automatique des langues (tal) pour dépasser la représentation classique des textes en sac de mots. Ensuite, deux scores sont calculés pour rendre compte du degré de similarité entre deux contenus : l'un relatif à leur similarité thématique, basé sur un modèle vectoriel de ri; l'autre à leur proximité temporelle, basé sur une fonction empirique. Finalement, un modèle de classification appris à partir de paires de documents, décrites par ces deux scores et annotées manuellement, permet d'ordonnancer les résultats.L'évaluation des performances du système a elle aussi fait l'objet de questionnements dans ces travaux de thèse. Les contraintes imposées par les données traitées et le besoin particulier de l'entreprise partenaire nous ont en effet contraints à adopter une alternative au protocole classique d'évaluation en ri, le paradigme de CranfieldThe goal of this thesis, conducted within an industrial framework, is to pair textual media content. Specifically, the aim is to pair on-line news articles to relevant videos for which we have a textual description. The main issue is then a matter of textual analysis, no image or spoken language analysis was undertaken in the present study. The question that arises is how to compare these particular objects, the texts, and also what criteria to use in order to estimate their degree of similarity. We consider that one of these criteria is the topic similarity of their content, in other words, the fact that two documents have to deal with the same topic to form a relevant pair. This problem fall within the field of information retrieval (ir) which is the main strategy called upon in this research. Furthermore, when dealing with news content, the time dimension is of prime importance. To address this aspect, the field of topic detection and tracking (tdt) will also be explored.The pairing system developed in this thesis distinguishes different steps which complement one another. In the first step, the system uses natural language processing (nlp) methods to index both articles and videos, in order to overcome the traditionnal bag-of-words representation of texts. In the second step, two scores are calculated for an article-video pair: the first one reflects their topical similarity and is based on a vector space model; the second one expresses their proximity in time, based on an empirical function. At the end of the algorithm, a classification model learned from manually annotated document pairs is used to rank the results.Evaluation of the system's performances raised some further questions in this doctoral research. The constraints imposed both by the data and the specific need of the partner company led us to adapt the evaluation protocol traditionnal used in ir, namely the cranfield paradigm. We therefore propose an alternative solution for evaluating the system that takes all our constraints into account
48
Yang, Chin-Hung, and 楊欽弘. "The Information Content of Stock Recommendations in Financial Press." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/90474660170327945709.
Full textAbstract:
碩士國立雲林科技大學
財務金融系碩士班
93
According to the past relevant literature about the impact of security recommendations on stock prices, mostly use market model of event study and estimate the regression coefficients with ordinary least square (OLS) method on empirical work. The OLS method supposes variance to be constant neglects the change of risk, so this research is adopted by Bollerslev(1986) propose that generalized autoregressive conditional heteroscedasticity (ARCH) method to estimate the regression coefficients and compare the difference of the two method to able be have breakthrough discoveries. On the other hand according to the recent research point out, the information sources that stock investors of Taiwan consult most frequently is a professional newspaper, so this paper discuss do the stock recommendations have investment value to investors in Commercial Times News and Economic Daily News each Sunday. Our sample period includes from January 1, 2004 to December 31, 2004. The result of study is as follows: 1、Both OLS method and GARCH method have a statistically significant negative abnormal return on event day, so if investors buy the security according to the stock recommendations may suffer the loss. 2、In event day, abnormal return have statistically significant difference in different industry under OLS method and GARCH method. 3、In event day, abnormal return have statistically significant difference in different firm size under OLS method and GARCH method. 4、In event day, abnormal return have statistically significant difference in different recommending times in OLS method, but insignificant under GARCH method.
49
Chiu, Shao-Ching, and 邱紹卿. "Information Content of Investment Recommendations by Foreign Institutional Investors." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/66754162415718867478.
Full textAbstract:
碩士國立中興大學
財務金融學系
93
The main objective of this paper is to examine the impacts of foreign investors’ investment ratings announcement on the abnormal stock prices’ fluctuations and the duration of such abnormal fluctuations, also the trading strategies adopted by foreign investors before and after the announcement. In addition to this, this paper also attempts to find out any relations between the aforesaid ratings and the fundamental of a company. Our findings show that CAAR are significant regardless of before or after the announcement. Moreover, the abnormal returns last for several months after the announcement, indicating that foreign investors’ ratings have provided information content to investors. With respect to trading strategy, the findings show that foreign investors will either purchase or sell stocks in advance of the announcement of up-rating or de-rating events. With regard to the fundamental, the company’s performances in revenue and profit are better than the anticipated when the rating is up; contrarily, the company’s performances are poorer than the anticipated when the rating is down. Finally, the findings also show that the debt ratios of up-rated companies tend to increase whereas those for de-rated companies move oppositely.
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Fang, Jiang-Kai, and 方建凱. "The Information Content of Stock Recommendations of Foreign Security Firms." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/06354970082497556542.
Full textAbstract:
碩士國立高雄大學
金融管理學系碩士班
100
This study explores the information content of stock recommendations of foreign security firms. We collect the investment recommendation reports from daily newspapers in Taiwan in order to increase the visibility of stock recommendations and classify the recommendation words into three levels without subjective adjudgment : upgrade、downgrade and neutral. The main purpose of this study examines whether or not the conflict of interest exists and the market responds before and after the reports. Previous studies show that the market returns are negative after the reports and conflict of interest do exist. However, our results indicate that the market responds positively to the upgrades and downgrades recommendations. In addition, we find that these firms might buy (sell) in advance before issuing upgrades (downgrades) recommendations. Lastly, we find no evidence that conflicts of interest exist after the reports, they trade consistently with their recommendations after the reports.