Searched for: subject%3A%22recommender%255C%2Bsystems%22
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Muetzel, Hannah (author)
When discussing media, parasocial phenomena is mentioned. Audience members naturally partake in parasocial phenomena when consuming media and empathizing with media figures. Viewers who form relationships with a media figure despite never meeting them are said to be in a parasocial relationship. On YouTube, examples of parasocial interactions...
master thesis 2021
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Bánsági, Aurél (author)
In group recommendation, a key question is how preferences from individuals should be obtained and then aggregated into a group outcome. Collecting individual preferences can be done through implicit or explicit means, but there is insufficient research available on what option is optimal. For comparing different possible aggregation strategies,...
master thesis 2021
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van Ramshorst, Arjo (author)
In recent years, recommender systems have become a fundamental part of our online experience. Users rely on such systems in situations with many potential choices, such as watching a movie on a streaming service, reading a blog post, or listening to a song. Traditionally, these systems use techniques such as collaborative filtering and content...
master thesis 2021
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Bobde, Sharwin (author)
Using Recommender Systems with Evolutionary Algorithms is an extremely niche domain. It holds the key to enabling new user interaction designs, where users can effectively configure their experience with a Recommender System. This thesis answers important questions about the scientific aspects of its application to large-scale data through a...
master thesis 2021
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Kim, Jaehun (author)
Machine learning (ML) has become a core technology for many real-world applications. Modern ML models are applied to unprecedentedly complex and difficult challenges, including very large and subjective problems. For instance, applications towards multimedia understanding have been advanced substantially. Here, it is already prevalent that...
doctoral thesis 2021
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Ebrahimi Fard, A. (author)
The phenomenon of rumour spreading refers to a collective process where people participate in the transmission of unverified and relevant information to make sense of the ambiguous, dangerous, or threatening situation. The dissemination of rumours on a large scale no matter with what purpose could precipitate catastrophic repercussions. This...
doctoral thesis 2021
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Li, Roger Zhe (author), Urbano, Julián (author), Hanjalic, A. (author)
In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. In this paper we focus on the so-called mainstream bias: the tendency of a recommender system to provide better recommendations to users who have a mainstream taste, as opposed to non-mainstream users. We propose NAECF,...
conference paper 2021
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Salmi, Salim (author), Mérelle, Saskia (author), Brinkman, W.P. (author)
Background: The working environment of a suicide prevention helpline requires high emotional and cognitive awareness from chat counselors. A shared opinion among counselors is that as a chat conversation becomes more difficult, it takes more effort and a longer amount of time to compose a response, which, in turn, can lead to writer's block....
journal article 2021
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Mulder, M. (author), Inel, O. (author), Oosterman, J.E.G. (author), Tintarev, N. (author)
Diversity in personalized news recommender systems is often defined as dissimilarity, and operationalized based on topic diversity (e.g., corona versus farmers strike). Diversity in news media, however, is understood as multiperspectivity (e.g., different opinions on corona measures), and arguably a key responsibility of the press in a...
conference paper 2021
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Li, Roger Zhe (author), Urbano, Julián (author), Hanjalic, A. (author)
Direct optimization of IR metrics has often been adopted as an approach to devise and develop ranking-based recommender systems. Most methods following this approach (e.g. TFMAP, CLiMF, Top-N-Rank) aim at optimizing the same metric being used for evaluation, under the assumption that this will lead to the best performance. A number of studies...
conference paper 2021
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Vrijenhoek, Sanne (author), Kaya, M. (author), Metoui, N. (author), Möller, Judith (author), Odijk, Daan (author), Helberger, Natali (author)
News recommenders help users to find relevant online content and have the potential to fulfilla crucial role in a democratic society, directing the scarce attention of citizens towards the information that is most important to them. Simultaneously, recent concerns about so-called filter bubbles, misinformation and selective exposure are...
conference paper 2021
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Abou Eddahab, F. (author)
doctoral thesis 2020
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Mulder, Mats (author)
Previous research on diversity in recommender systems define diversity as the opposite of similarity and propose methods that are based on topic diversity. Diversity in news media, however, is understood as multiperspectivity and scholars generally agree that fostering diversity is the key responsibility of the press in a democratic society....
master thesis 2020
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Gong, B. (author)
With advancements in Internet and technology, it has become increasingly easy for people to enjoy music. Users are able to access millions of songs through music streaming services like Spotify, Pandora, and Deezer. Access to such large catalogs created a need for relevant song recommendations. Music recommender systems assist users in finding...
master thesis 2020
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van der Goes, Maurits (author)
The globalizing economy with its new goods and services, knowledge spread, and competition for talent is an increasing complexity for organizations, which requires organizations to adapt more quickly. Organizations are essential to society, as people are more productive in groups. For their continuity, it is important that organizations...
master thesis 2020
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Knyazev, Norman (author)
Many widely used Recommender System algorithms estimate user tastes without accounting for their evolving nature. In recent years there has been a gradual increase in methods incorporating such temporal dynamics through sequential processing of user consumption histories. Some works have also included additional temporal features such as time...
master thesis 2020
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Dingjan, Mitchell (author)
Recommender systems focus on automatically surfacing suitable items for users from digital collections that are too large for the user to oversee themselves. A considerable body of work exists on surfacing items that match what a user liked in the past; this way, the recommender system will exploit its knowledge of a user's comfort zone. However...
master thesis 2020
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Danaf, Mazen (author), Guevara, Angelo (author), Atasoy, B. (author), Ben-Akiva, Moshe (author)
Endogeneity arises in discrete choice models due to several factors and results in inconsistent estimates of the model parameters. In adaptive choice contexts such as choice-based recommender systems and adaptive stated preferences (ASP) surveys, endogeneity is expected because the attributes presented to an individual in a specific menu (or...
journal article 2020
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Salmi, Salim (author)
STUDY OBJECTIVE: Suicide crisis chat counsellors work in an environment which de- mands high emotional and cognitive awareness. A shared opinion among counsellors is that as the chat conversation turns more difficult it takes longer and more effort to come up with a response. Supportive technology might resolve this ”writer’s block” by giving...
master thesis 2019
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Krishnaraj, Manoj (author)
Recommender systems (RS) often use a large amount of data for a marginal gain in performance. This thesis investigates the data minimization in Recommender Systems, which is not well studied in the literature. This thesis extends the data minimization principles advocated in GDPR and studies its effects on recommender systems. Minimizing data...
master thesis 2019
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