Дисертації з теми "Content recommendations"
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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.
Повний текст джерелаDias, 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.
Повний текст джерелаInformation 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.
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.
Повний текст джерелаBelin, 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.
Повний текст джерелаAndersson, 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.
Повний текст джерелаMängden 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.
Дячук, Іван Сергійович. "Інтелектуальна система підбору клієнтського контенту". Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/25528.
Повний текст джерелаThe 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.
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.
Повний текст джерелаSå 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.
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.
Повний текст джерелаDurant 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.
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.
Повний текст джерелаGibała, 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.
Повний текст джерелаGeoghegan, 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.
Повний текст джерелаPeri-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.
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/.
Повний текст джерелаSmaaberg, 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.
Повний текст джерелаKirmemis, Oznur. "Openmore: A Content-based Movie Recommendation System." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609479/index.pdf.
Повний текст джерелаSILVA, 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.
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.
Повний текст джерелаReis, 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.
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.
Повний текст джерелаJiang, 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.
Повний текст джерелаpublished_or_final_version
Computer Science
Doctoral
Doctor of Philosophy
Horsburgh, Ben. "Integrating content and semantic representations for music recommendation." Thesis, Robert Gordon University, 2013. http://hdl.handle.net/10059/859.
Повний текст джерелаCosta, Thales Filizola. "Taxonomy-driven content-based recommendation for new itens." Universidade Federal de Minas Gerais, 2014. http://hdl.handle.net/1843/ESBF-9KJR2D.
Повний текст джерелаThollot, Raphaël. "Dynamic situation monitoring and Context-Aware BI recommendations." Phd thesis, Ecole Centrale Paris, 2012. http://tel.archives-ouvertes.fr/tel-00718917.
Повний текст джерелаGuillou, Frédéric. "On recommendation systems in a sequential context." Thesis, Lille 3, 2016. http://www.theses.fr/2016LIL30041/document.
Повний текст джерелаThis 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
Agagu, Tosin. "Recommendation Approaches Using Context-Aware Coupled Matrix Factorization." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/37012.
Повний текст джерелаLagerqvist, 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.
Повний текст джерелаAkermi, Imen. "A hybrid model for context-aware proactive recommendation." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30101/document.
Повний текст джерелаJust-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
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.
Повний текст джерелаMeguebli, Youssef. "Leveraging User-Generated Content for Enhancing and Personalizing News Recommendation." Thesis, CentraleSupélec, 2015. http://www.theses.fr/2015SUPL0007/document.
Повний текст джерелаIn 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
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.
Повний текст джерелаSong, Songbo. "Advanced personalization of IPTV services." Thesis, Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0001/document.
Повний текст джерелаInternet 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
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.
Повний текст джерелаNguyen, Ngoc Chan. "Service recommendation for individual and process use." Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00789726.
Повний текст джерелаAl-Ghossein, Marie. "Context-aware recommender systems for real-world applications." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT008/document.
Повний текст джерелаRecommender 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
McParlane, Philip. "The role of context in image annotation and recommendation." Thesis, University of Glasgow, 2016. http://theses.gla.ac.uk/7676/.
Повний текст джерелаNelaturu, 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.
Повний текст джерелаLandia, Nikolas. "Content-awareness and graph-based ranking for tag recommendation in folksonomies." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/58069/.
Повний текст джерелаSomasundaram, Jyothilakshmi. "Releasing Recommendation Datasets while Preserving Privacy." Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1306427987.
Повний текст джерелаLotz-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.
Повний текст джерелаThis 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.
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.
Повний текст джерелаInternet 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.
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.
Повний текст джерелаBambia, Meriam. "Jointly integrating current context and social influence for improving recommendation." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30110/document.
Повний текст джерелаDue 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
Zanarella, Leonardo. "Progettazione ed Implementazione di Recommendation Content-based Filtering basato su Apache Mahout." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
Знайти повний текст джерелаKaraman, 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.
Повний текст джерелаWhich 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.
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.
Повний текст джерелаSistemas 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.
Lokesh, Ashwini. "A Comparative Study of Recommendation Systems." TopSCHOLAR®, 2019. https://digitalcommons.wku.edu/theses/3166.
Повний текст джерелаDong, 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.
Повний текст джерелаDésoyer, 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.
Повний текст джерелаThe 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
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.
Повний текст джерела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.
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.
Повний текст джерела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.
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.
Повний текст джерела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.