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Academic literature on the topic 'Content recommendations'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Content recommendations"

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Lidén, Erik R. "Swedish Stock Recommendations: Information Content or Price Pressure?" Multinational Finance Journal 11, no. 3/4 (December 1, 2007): 253–85. http://dx.doi.org/10.17578/11-3/4-4.

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Han, Jonghyun, Hedda R. Schmidtke, Xing Xie, and Woontack Woo. "Adaptive content recommendation for mobile users: Ordering recommendations using a hierarchical context model with granularity." Pervasive and Mobile Computing 13 (August 2014): 85–98. http://dx.doi.org/10.1016/j.pmcj.2013.11.002.

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Yoo, Youngtae, and Hyunjun Park. "The Informational Content Of Changes In Stock Recommendation: Chaebol Vs. Non-Chaebol Affiliated Analysts." Journal of Applied Business Research (JABR) 32, no. 6 (November 2, 2016): 1687. http://dx.doi.org/10.19030/jabr.v32i6.9816.

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Accurate analysts’ reports alleviate information asymmetry between companies and investors by providing accounting information that is useful in investment decision-making for market participants. Investors evaluate the credibility of stock recommendations based on the accuracy of the earnings forecasts of analysts, applying them in the decision-making process. Studies of stock recommendations have focused on their informational content, systematically analyzing the characteristics of recommendations and, to a lesser degree, decision-making factors. For most analysts, when stock recommendations and forecast changes are simultaneously disclosed, a large bias results if analysts fail to consider the magnitude of the market reaction relative to the earnings forecast and stock recommendations. In most previous studies, the informational content of both individual stock recommendations and changes in stock recommendations was investigated. In this study, we examine differences in the informational content depending on the stock recommendations of the report released immediately previous to the current report for the same recommendation. An upgraded (or downgraded) revision within the same recommendation category is associated with a greater (lower) stock price return. Even the same recommendation in the market may cause different reactions depending on both the recommendation itself and on the direction of change of the recommendation. Affiliated analysts have more access to inside information of the companies they analyze. The stock returns after revisions of Chaebol-affiliated analysts are significantly higher than those of non-Chaebol-affiliated analysts.
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Jabbar, Muhammad, Qaisar Javaid, Muhammad Arif, Asim Munir, and Ali Javed. "An Efficient and Intelligent Recommender System for Mobile Platform." October 2018 37, no. 4 (October 1, 2018): 463–80. http://dx.doi.org/10.22581/muet1982.1804.02.

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Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item. Recommender systems are used to provide recommendations in many domains such as movies, books, digital equipment’s, etc. The massive collection of available books online presents a great challenge for users to select the relevant books that meet their preferences. Users usually read few pages or contents to decide whether to buy a certain book or not. Recommender systems provide different value addition factors such as similar user ratings, users past history, user profiles, etc. to facilitate the users in terms of providing relevant recommendations according to their preferences. Recommender systems are broadly categorized into content based approach and collaborative filtering approach. Content based or collaborative filtering approaches alone are not sufficient to provide most accurate and relevant recommendations under diverse scenarios. Therefore, hybrid approaches are also designed by combining the features of both the content based and collaborative filtering approaches to provide more relevant recommendations. This paper proposes an efficient hybrid recommendation scheme for mobile platform that includes the traits of content based and collaborative filtering approaches in addition of the context based approach that is included to provide the latest books recommendations to user.Objective and subjective evaluation measures are used to compute the performance of the proposed system. Experimental results are promising and signify the effectiveness of our proposed hybrid scheme in terms of most relevant and latest books recommendations.
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Varada, Sowmya, Ronilda Lacson, Ali S. Raja, Ivan K. Ip, Louise Schneider, David Osterbur, Paul Bain, et al. "Characteristics of knowledge content in a curated online evidence library." Journal of the American Medical Informatics Association 25, no. 5 (October 27, 2017): 507–14. http://dx.doi.org/10.1093/jamia/ocx092.

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Abstract Objective To describe types of recommendations represented in a curated online evidence library, report on the quality of evidence-based recommendations pertaining to diagnostic imaging exams, and assess underlying knowledge representation. Materials and Methods The evidence library is populated with clinical decision rules, professional society guidelines, and locally developed best practice guidelines. Individual recommendations were graded based on a standard methodology and compared using chi-square test. Strength of evidence ranged from grade 1 (systematic review) through grade 5 (recommendations based on expert opinion). Finally, variations in the underlying representation of these recommendations were identified. Results The library contains 546 individual imaging-related recommendations. Only 15% (16/106) of recommendations from clinical decision rules were grade 5 vs 83% (526/636) from professional society practice guidelines and local best practice guidelines that cited grade 5 studies (P < .0001). Minor head trauma, pulmonary embolism, and appendicitis were topic areas supported by the highest quality of evidence. Three main variations in underlying representations of recommendations were “single-decision,” “branching,” and “score-based.” Discussion Most recommendations were grade 5, largely because studies to test and validate many recommendations were absent. Recommendation types vary in amount and complexity and, accordingly, the structure and syntax of statements they generate. However, they can be represented in single-decision, branching, and score-based representations. Conclusion In a curated evidence library with graded imaging-based recommendations, evidence quality varied widely, with decision rules providing the highest-quality recommendations. The library may be helpful in highlighting evidence gaps, comparing recommendations from varied sources on similar clinical topics, and prioritizing imaging recommendations to inform clinical decision support implementation.
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Afolabi, Ibukun Tolulope, Opeyemi Samuel Makinde, and Olufunke Oyejoke Oladipupo. "Semantic Web mining for Content-Based Online Shopping Recommender Systems." International Journal of Intelligent Information Technologies 15, no. 4 (October 2019): 41–56. http://dx.doi.org/10.4018/ijiit.2019100103.

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Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.
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Javed, Umair, Kamran Shaukat, Ibrahim A. Hameed, Farhat Iqbal, Talha Mahboob Alam, and Suhuai Luo. "A Review of Content-Based and Context-Based Recommendation Systems." International Journal of Emerging Technologies in Learning (iJET) 16, no. 03 (February 12, 2021): 274. http://dx.doi.org/10.3991/ijet.v16i03.18851.

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In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.
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Sivanaiah, Rajalakshmi, R. Sakaya Milton, and T. T. Mirnalinee. "Content boosted hybrid filtering for solving pessimistic user problem in recommendation systems." Intelligent Data Analysis 24, no. 6 (December 18, 2020): 1477–96. http://dx.doi.org/10.3233/ida-205244.

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The main goal of a recommendation system is to recommend items of interest to users by analyzing their historical data. Content-based and collaborative filtering are the traditional recommendation strategies, each with its own strengths and weaknesses. Some of their weaknesses can be overcome by combining the two strategies. The resulting hybrid system performs qualitatively better than the traditional recommendation systems. However, historical data of some users may consist largely of only likes or only dislikes. Those users are termed as optimistic or pessimistic users respectively. On an average there are around 10 to 20% of pessimistic users present in a given dataset. For pessimistic users, whose profiles have mostly dislikes and very few likes, content-based filtering can hardly recommend any items of interest. In content-based filtering technique pessimistic users get poor recommendations of either uninteresting movies or no recommendations at all. This can be alleviated by boosting the content profiles of pessimistic users using the top-n recommendations of collaborative filtering. This content boosted hybrid filtering system provides a novel list of recommendations even for pessimistic users, with predictive accuracy better than that of a traditional content-based filtering system.
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Aloudat, M., A. Papp, N. Magyar, L. Simon Sarkadi, and A. Lugasi. "Nutritional Value of Traditional and Modern Meals: Jordan and Hungary." Acta Alimentaria 49, no. 4 (November 7, 2020): 491–97. http://dx.doi.org/10.1556/066.2020.49.4.15.

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The purpose of this study was to compare the energy content and macronutrients of forty main popular traditional and modern meals in both Jordan and Hungary with the national and international recommendations. The calculation of energy content and macronutrients were done on traditional and modern recipes by two different softwares (ESHA and NutriComp). Neither Jordanian nor Hungarian foods met the recommended energy content (35% of daily energy intake, 8400 kJ for energy intake). The recipes of both nations are characterised by higher protein, fat, and salt contents than WHO recommendation, a lower fibre content, and sugar content within the recommended limits. The fat energy ratio and saturated fatty acid content of Hungarian recipes are significantly higher than WHO recommendation. In general, Jordanian meals were more likely to meet the inclusion criteria. In conclusion, neither Jordanian nor Hungarian traditional and popular meals meet the international nutritional recommendations for a healthy diet, however, the composition of the real dishes may differ significantly from the recipes depending on the available ingredients and chosen kitchen technology.
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Zadro, Joshua, Aimie L. Peek, Rachael H. Dodd, Kirsten McCaffery, and Christopher Maher. "Physiotherapists’ views on the Australian Physiotherapy Association’s Choosing Wisely recommendations: a content analysis." BMJ Open 9, no. 9 (September 2019): e031360. http://dx.doi.org/10.1136/bmjopen-2019-031360.

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ObjectivesChoosing Wisely holds promise for increasing awareness of low-value care in physiotherapy. However, it is unclear how physiotherapists’ view Choosing Wisely recommendations. The aim of this study was to evaluate physiotherapists’ feedback on Choosing Wisely recommendations and investigate agreement with each recommendation.SettingThe Australian Physiotherapy Association emailed a survey to all 20 029 physiotherapist members in 2015 seeking feedback on a list of Choosing Wisely recommendations.ParticipantsA total of 9764 physiotherapists opened the email invitation (49%) and 543 completed the survey (response rate 5.6%). Participants were asked about the acceptability of the wording of recommendations using a closed (Yes/No) and free-text response option (section 1). Then using a similar response format, participants were asked whether they agreed with each Choosing Wisely recommendation (sections 2–6).Primary and secondary outcomesWe performed a content analysis of free-text responses (primary outcome) and used descriptive statistics to report agreement and disagreement with each recommendation (secondary outcome).ResultsThere were 872 free-text responses across the six sections. A total of 347 physiotherapists (63.9%) agreed with the ‘don’t’ style of wording. Agreement with recommendations ranged from 52.3% (electrotherapy for back pain) to 76.6% (validated decision rules for imaging). The content analysis revealed that physiotherapists felt that blanket rules were inappropriate (range across recommendations: 13.9%–30.1% of responses), clinical experience is more valuable than evidence (11.7%–28.3%) and recommendations would benefit from further refining or better defining key terms (7.3%–22.4%).ConclusionsAlthough most physiotherapists agreed with both the style of wording for Choosing Wisely recommendations and with the recommendations, their feedback highlighted a number of areas of disagreement and suggestions for improvement. These findings will support the development of future recommendations and are the first step towards increasing the impact Choosing Wisely has on physiotherapy practice.
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Dissertations / Theses on the topic "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.

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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 Informática
Information 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 easier
Durant 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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Books on the topic "Content recommendations"

1

International Business Machines Corporation. International Technical Support Organization, ed. IBM FileNet content manager implementation best practices and recommendations. [United States?]: IBM, International Technical Support Organization, 2008.

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Ontario. Ministry of the Environment. Lead in Soil Committee. Review and recommendations on a lead in soil guideline. [Toronto]: The Ministry, 1987.

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Carbon credits from peatland rewetting: Climate, biodiversity, land use : science, policy, implementation, and recommendations of a pilot project in Belarus. Stuttgart: Schweizerbart Science Publishers, 2011.

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Johnson, Art. Carbaryl concentrations in Willapa Bay and recommendations for water quality guidelines. Olympia, Wash: Washington State Department of Ecology, Environmental Assessment Program, 2001.

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Neil, James. Scottish further education development plans: A study into their style, content and methodology, with recommendations. [Edinburgh?]: [Scottish Education Department?], 1989.

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Rice, Patricia Ohl. The accreditation of library and information science education: A content analysis of COA recommendations, 1973-1985. Ann Arbor, Mich: University Microfilms International, 1986.

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Pilgrim, John D. Threatened and alien species in Vietnam: Background and recommendations for the content of the national biodiversity law. Hanoi: [BirdLife International Global Forest Policy Project], 2007.

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Massachusetts. Dept. of Public Health. State Laboratory Institute. A statewide survey of lead in school drinking water: An estimate of the prevalence of elevated lead levels in drinking water and recommendations for remedial action to reduce exposure to lead : executive summary. Boston (305 South St., Jamaica Plain, MA 02130): Massachusetts Dept. of Public Health, 1988.

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Colloquium, International Potash Institute. Development of K-fertilizer recommendations: 22nd Colloquium of the International Potash Institute, Soligorsk, USSR, June 18-23, 1990. Bern, Switzerland: The Institute, 1990.

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Colloquium, International Potash Institute. Development of K-fertilizer recommendations: 22nd Colloquium of the International Potash Institute, Soligorsk, USSR, June 18-23, 1990. Bern, Switzerland: Internaitonal Potash Institute, 1990.

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Book chapters on the topic "Content recommendations"

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Srifi, Mehdi, Badr Ait Hammou, Ayoub Ait Lahcen, and Salma Mouline. "A Concise Survey on Content Recommendations." In Communications in Computer and Information Science, 393–405. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96292-4_31.

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Lee, Keonsoo, and Yunyoung Nam. "Persuading Recommendations Using Customized Content Curation." In Lecture Notes in Electrical Engineering, 159–63. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5041-1_28.

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Vahidi Ferdousi, Zahra, Dario Colazzo, and Elsa Negre. "CBPF: Leveraging Context and Content Information for Better Recommendations." In Advanced Data Mining and Applications, 381–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05090-0_32.

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Guo, Weisen, and Steven B. Kraines. "Semantic Content-Based Recommendations Using Semantic Graphs." In Advances in Experimental Medicine and Biology, 653–59. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5913-3_72.

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Dietz, Linus W., Sameera Thimbiri Palage, and Wolfgang Wörndl. "Navigation by Revealing Trade-offs for Content-Based Recommendations." In Information and Communication Technologies in Tourism 2022, 149–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94751-4_14.

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AbstractConversational recommender systems have been introduced to provide users the opportunity to give feedback on items in a turn-based dialog until a final recommendation is accepted. Tourism is a complex domain for recommender systems because of high cost of recommending a wrong item and often relatively few ratings to learn user preferences. In a scenario such as recommending a city to visit, conversational content-based recommendation may be advantageous, since users often struggle to specify their preferences without concrete examples. However, critiquing item features comes with challenges. Users might request item characteristics during recommendation that do not exist in reality, for example demanding very high item quality for a very low price. To tackle this problem, we present a novel conversational user interface which focuses on revealing the trade-offs of choosing one item over another. The recommendations are driven by a utility function that assesses the user’s preference toward item features while learning the importance of the features to the user. This enables the system to guide the recommendation through the search space faster and accurately over prolonged interaction. We evaluated the system in an online study with 600 participants and find that our proposed paradigm leads to improved perceived accuracy and fewer conversational cycles compared to unit critiquing.
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Dragan, Łukasz, and Anna Wróblewska. "Content-Based Recommendations in an E-Commerce Platform." In Advances in Intelligent Systems and Computing, 252–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18058-4_20.

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Almuhaimeed, Abdullah, and Maria Fasli. "Exploiting Different Bioinformatics Resources for Enhancing Content Recommendations." In Lecture Notes in Computer Science, 558–61. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08245-5_50.

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Ivanova, Iustina, Marina Andrić, and Francesco Ricci. "Content-Based Recommendations for Crags and Climbing Routes." In Information and Communication Technologies in Tourism 2022, 369–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94751-4_33.

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AbstractClimbing is a popular sport for active tourists and recreational sportsmen. Alpine climbing areas, such as the Alps, can attract tourists from all over the world. Various websites, mobile applications, and books are used by climbers to obtain information on important aspects of the available climbing routes, including their properties, location, and especially their difficulty. Considering this large amount of information and options, it is in reality difficult for climbers to properly select which routes to climb. Hence, we propose recommendation technologies aimed at supporting climbers in this decision task. The developed system prototype constructs a climber’s profile with preferences derived from climber’s logbook data collected by a mobile app. Then, the system can recommend suitable crags and climbing routes within the selected crags. The designed interface and the basic computational models for such a system prototype are presented. The proposed technology aims at complementing existing electronic climbing guidebooks and providing decision support to climbers.
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White, Philip J., Michael J. Bell, Ivica Djalovic, Philippe Hinsinger, and Zed Rengel. "Potassium Use Efficiency of Plants." In Improving Potassium Recommendations for Agricultural Crops, 119–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59197-7_5.

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AbstractThere are many terms used to define aspects of potassium (K) use efficiency of plants. The terms used most frequently in an agricultural context are (1) agronomic K use efficiency (KUE), which is defined as yield per unit K available to a crop and is numerically equal to the product of (2) the K uptake efficiency (KUpE) of the crop, which is defined as crop K content per unit K available and (3) its K utilization efficiency (KUtE), which is defined as yield per unit crop K content. There is considerable genetic variation between and within plant species in KUE, KUpE, and KUtE. Root systems of genotypes with greatest KUpE often have an ability (1) to exploit the soil volume effectively, (2) to manipulate the rhizosphere to release nonexchangeable K from soil, and (3) to take up K at low rhizosphere K concentrations. Genotypes with greatest KUtE have the ability (1) to redistribute K from older to younger tissues to maintain growth and photosynthesis and (2) to reduce vacuolar K concentration, while maintaining an appropriate K concentration in metabolically active subcellular compartments, either by anatomical adaptation or by greater substitution of K with other solutes in the vacuole. Genetic variation in traits related to KUpE and KUtE might be exploited in breeding crop genotypes that require less K fertilizer. This could reduce fertilizer costs, protect the environment, and slow the exhaustion of nonrenewable resources.
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Wang, Haoran, Zhengzhong Zhou, Changcheng Xiao, and Liqing Zhang. "Content Based Image Search for Clothing Recommendations in E-Commerce." In Multimedia Data Mining and Analytics, 253–67. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_11.

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Conference papers on the topic "Content recommendations"

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Bogaards, Niels, and Frederique Schut. "Content-based book recommendations." In RecSys '21: Fifteenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460231.3474603.

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Zhang, Yang, and Qiang Ma. "Citation Recommendations Considering Content and Structural Context Embedding." In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2020. http://dx.doi.org/10.1109/bigcomp48618.2020.0-109.

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Gautam, Anjali, Parila Chaudhary, Kunal Sindhwani, and Punam Bedi. "CBCARS: Content boosted context-aware recommendations using tensor factorization." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732028.

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Nessel, Jochen, and Barbara Cimpa. "The MovieOracle - Content Based Movie Recommendations." In 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2011. http://dx.doi.org/10.1109/wi-iat.2011.236.

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Stecher, Rodolfo, Gianluca Demartini, and Claudia Niederée. "Social recommendations of content and metadata." In the 10th International Conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1497308.1497329.

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Wang, Yiwen, Natalia Stash, Lora Aroyo, Laura Hollink, and Guus Schreiber. "Semantic relations for content-based recommendations." In the fifth international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1597735.1597786.

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Kolobov, Oleg S., Anna A. Knyazeva, Yulia V. Leonova, and Igor Yu Turchanovsky. "Personalizing digital services as exemplified by library recommendation service." In Twenty Fifth International Conference and Exhibition «LIBCOM-2021». Russian National Public Library for Science and Technology, 2022. http://dx.doi.org/10.33186/978-5-85638-247-0-2022-35-40.

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The possibility of designing recommendation system for library e-catalog as a recommendation service is examined. Several options for recommendations were considered, i.e. collaborative filtering method and content-based recommendations. The findings were used for building the recommendation service based on two recommendation algorithms – document-based collaborative filtering and content-based recommendations. Anonymized data on fulfilled orders and library’s e-catalog data are used as input data for recommendation system.
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Herzog, Daniel, and Wolfgang Wörndl. "Extending Content-Boosted Collaborative Filtering for Context-aware, Mobile Event Recommendations." In 12th International Conference on Web Information Systems and Technologies. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005763702930303.

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Veas, Eduardo, Belgin Mutlu, Cecilia di Sciascio, Gerwald Tschinkel, and Vedran Sabol. "Visual Recommendations for Scientific and Cultural Content." In International Conference on Information Visualization Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005352802560261.

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Wen, Hongyi, Longqi Yang, and Deborah Estrin. "Leveraging post-click feedback for content recommendations." In RecSys '19: Thirteenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3298689.3347037.

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Reports on the topic "Content recommendations"

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NATIONAL RESEARCH COUNCIL WASHINGTON DC. Recommendations for Content Revision and Alternate Delivery Modes for the Human Engineering Guide to Equipment Design (HEGED). Fort Belvoir, VA: Defense Technical Information Center, March 1985. http://dx.doi.org/10.21236/ada155781.

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Haßler, Björn, and Gesine Haseloff. TVET Research in SSA: Recommendations for Thematic Priorities. Undefined, February 2022. http://dx.doi.org/10.53832/opendeved.0268.

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This paper builds on our study on research on Technical and Vocational Education and Training (TVET) in sub-Saharan Africa (SSA). The study was commissioned by the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) and was conducted in 2019. Our study undertook a systematic literature review, which evaluated over 2,000 scientific publications, classifying some 300 as relevant to the topic and examined them in greater detail. One focus area was the institutionalisation of research on TVET in sub-Saharan Africa, its actors, networks and funding. A second area focused on the topics and content addressed by researchers on TVET in SSA. Our comprehensive study allows us to derive indications for future TVET research in SSA, which form the content of the present paper. Our recommendations for future research emerge from: The research literature analysed; A series of expert interviews; and A Structured Community Review. Our key contribution is an extension of the Mulder-Roelofs Vocational Education and Training Research Framework of categories for TVET research. The framework reflects the focus and quality criteria of European/German TVET, and is an ideal starting point. Through our research, we revised and extended the framework for use in SSA. Our goal was to build on international standards on the one hand, but on the other hand to extend and apply those for use in SSA. It therefore makes it possible to respond to the latest state of research both from a European perspective (e.g., donors/organisations like BMBF), as well as to examine the broad spectrum of very different aspects of TVET research in SSA. After presenting the relevant categories of TVET research, the authors go into detail on the topics for further research. Their conclusion recommends continuous research monitoring based on a constantly updated international network of researchers and institutions with an interest in TVET research in SSA. In particular, we propose a coordinated effort for an online, international community of stakeholders and researchers involved in TVET research in sub-Saharan Africa (https://convet.org).
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Silverman, D. J., M. A. Bauser, and R. D. Baird. Licensing an assured isolation facility for low-level radioactive waste. Volume 2: Recommendations on the content and review of an application. Office of Scientific and Technical Information (OSTI), July 1998. http://dx.doi.org/10.2172/665893.

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Moran, B., W. Belew, G. Hammond, and L. Brenner. Recommendations to the NRC on acceptable standard format and content for the Fundamental Nuclear Material Control (FNMC) Plan required for low-enriched uranium enrichment facilities. Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/5978296.

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Terrón-Caro, María Teresa, Rocio Cárdenas-Rodríguez, Fabiola Ortega-de-Mora, Kassia Aleksic, Sofia Bergano, Patience Biligha, Tiziana Chiappelli, et al. Policy Recommendations ebook. Migrations, Gender and Inclusion from an International Perspective. Voices of Immigrant Women, July 2022. http://dx.doi.org/10.46661/rio.20220727_1.

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This publication is the third product of the Erasmus + Project entitled Voices of Immigrant Women (Project Number: 2020-1-ES01-KA203-082364). This product is based on a set of policy recommendations that provides practical guidance on intervention proposals to those with political responsibilities in governance on migration management and policies for integration and social inclusion, as well as to policy makers in the governance of training in Higher Education (University) at all levels. This is intended to promote the development of practical strategies that allow overcoming the obstacles encountered by migrant women during the integration process, favoring the construction of institutions, administrations and, ultimately, more inclusive societies. The content presented in this book proposes recommendations and intervention proposals oriented to practice to: - Improve Higher Education study plans by promoting the training of students as future active protagonists who are aware of social interventions. This will promote equity, diversity and the integration of migrant women. - Strengthen cooperation and creation of networks between academic organizations, the third sector and public administrations that are responsible for promoting the integration and inclusion of migrant women. - Promote dialogue and the exchange of knowledge to, firstly, raise awareness of human mobility and gender in Europe and, secondly, promote the participation and social, labor and civic integration of the migrant population. All this is developed through 4 areas in which this book is articulated. The first area entitled "Migrant women needs and successful integration interventions"; the second area entitled "Promoting University students awareness and civic and social responsibility towards migrant women integration"; the third area entitled "Cooperation between Higher Education institutions and third sector"; the fourth and last area, entitled "Inclusive Higher Education".
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Johnson, Mark, and John Wachen. Examining Equity in Remote Learning Plans: A Content Analysis of State Responses to COVID-19. The Learning Partnership, November 2020. http://dx.doi.org/10.51420/report.2020.2.

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In this technical report, the authors present a content analysis of state guidance on remote learning from the 2019-20 school year. As schools across the country closed in response to COVID-19, state education agencies (SEAs) developed guidance for use by districts on how to ensure the continuation of education during the pandemic. The described analysis applied an equity framework that was developed based on concepts drawn from a literature review to examine the extent to which SEAs addressed issues of equity in their remote learning recommendations. The analysis revealed variation in the extent to which states explicitly focused on equity in their guidance. The analysis also identified exemplar states that encouraged local educators to keep equity at the forefront of their planning.
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Chang, Allan. Disclosure Standards of Large New Zealand Companies: A content analysis study of compliance with the FMA’s corporate governance guidelines. Unitec ePress, September 2017. http://dx.doi.org/10.34074/ocds.52017.

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This qualitative study is an attempt to gain some insights into the level of corporate governance disclosure in New Zealand. A sample of ten large publicly-listed companies was analysed to determine to what extent they fulfill the requirements of the corporate governance principles and guidelines as recommended by the Financial Markets Authority (FMA) of New Zealand. Even though compliance with the FMA’s recommendations is voluntary, a high overall percentage of compliance (74%) was recorded in this study. This indicates the seriousness with which New Zealand companies take investor concerns on issues of corporate governance.
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Tarasenko, Rostyslav O., Svitlana M. Amelina, and Albert A. Azaryan. Improving the content of training future translators in the aspect of studying modern CAT tools. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3877.

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The article deals with the search for improving the content of training for future translators, taking into account the expansion of the use of information technologies in the field of translation. The results of a study of curriculums for translators at the universities of Europe, America and Asia are presented. The use of CAT systems in the work of translation agencies is shown. The presentation of various CAT systems in training programs for translators and their use in the market of translation services is analyzed. It has been established that both university curricula and translation agencies are oriented, as a rule, not to one, but to several CAT systems. The results of a student survey based on their practice in translation agencies are presented. Recommendations have been developed regarding the inclusion of the most common CAT systems in the training program for translators. The expediency of studying not just one, but several CAT systems is substantiated. The necessity of studying both desktop and cloud CAT systems is indicated.
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Mintii, I. S. Using Learning Content Management System Moodle in Kryvyi Rih State Pedagogical University educational process. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3866.

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The article analyzes the results of the survey of 75 lecturers on using learning content management system (LCMS) Moodle in the educational process. It is defined that more than 75% of the respondents use LCMS Moodle. The lecturers up to 30 or over 60 years old, with up to 3-year-work experience in Universities need methodic assistance. Textual e-learning resources are widely used in developed courses while video and audio are not used enough. LCMS Moodle is mostly used during exams or tests and student work, and using LCMS Moodle should be intensified in lectures, laboratory and practical classes. Among the most demanded resources are label, page, file, URL, book, assignment, attendance, glossary, quiz. Thus, the popularization of other resources is identified as one of the most important. An action plan how to improve LCMS Moodle usage: increasing the IT competencies of both teachers and students – planned long-term courses “IT in full-time (blended) learning”; seminars, consultations, (group and individual forms) both on general issues, and according to the specificity of the specialties; methodic handouts and recommendations; improving logistics; improving logistical support – ensuring constant access to the Internet, updating and equipping computer classrooms; creating of transparent, predictable and attractive for authors content of the regulatory framework.
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Marienko, Maiia V., Yulia H. Nosenko, and Mariya P. Shyshkina. Personalization of learning using adaptive technologies and augmented reality. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4418.

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The research is aimed at developing the recommendations for educators on using adaptive technologies and augmented reality in personalized learning implementation. The latest educational technologies related to learning personalization and the adaptation of its content to the individual needs of students and group work are considered. The current state of research is described, the trends of development are determined. Due to a detailed analysis of scientific works, a retrospective of the development of adaptive and, in particular, cloud-oriented systems is shown. The preconditions of their appearance and development, the main scientific ideas that contributed to this are analyzed. The analysis showed that the scientists point to four possible types of semantic interaction of augmented reality and adaptive technologies. The adaptive cloud-based educational systems design is considered as the promising trend of research. It was determined that adaptability can be manifested in one or a combination of several aspects: content, evaluation and consistency. The cloud technology is taken as a platform for integrating adaptive learning with augmented reality as the effective modern tools to personalize learning. The prospects of the adaptive cloud-based systems design in the context of teachers training are evaluated. The essence and place of assistive technologies in adaptive learning systems design are defined. It is shown that augmented reality can be successfully applied in inclusive education. The ways of combining adaptive systems and augmented reality tools to support the process of teachers training are considered. The recommendations on the use of adaptive cloud-based systems in teacher education are given.
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