Searched for: subject%3A%22Collaborative%255C+filtering%22
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Harte, Jesse (author)
In this thesis we aim to research and design different neural models for session recommendation. We investigate the fundamental neural models for session recommendation, namely BERT4Rec, SASRec and GRU4Rec and subsequently use our findings to design a simpler but performant neural model. <br/><br/>Firstly, we address methodological errors made...
master thesis 2023
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Li, Roger Zhe (author)
doctoral thesis 2023
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Mundhra, Yash (author)
Recommender systems are an essential part of online businesses in today's day and age. They provide users with meaningful recommendations for items and products. A frequently occurring problem in recommender systems is known as the long-tail problem. It refers to a situation in which a majority of the items in the data set have limited ratings...
bachelor thesis 2022
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Monté, Sérénic (author)
Collaborative filtering is used to predict the preference or rating of a user for a certain item. Collaborative filtering is based on the notion that similar users rate similarly. A lot of research is done on how to improve this algorithm, mostly with deep learning. A less investigated field for recommender systems is graph signal processing....
bachelor thesis 2022
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Mariūnas, Karolis (author)
Recommender systems (RS) assist users in making decisions by filtering content that the user would likely find relevant. Standard techniques like collaborative filtering exploit user similarities to find the recommendations assuming that similar users are likely to be interested in the same items. On the other hand, graph RS borrow techniques...
bachelor thesis 2022
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Aiolli, Fabio (author), Conti, M. (author), Picek, S. (author), Polato, M. (author)
Nowadays, online services, like e-commerce or streaming services, provide a personalized user experience through recommender systems. Recommender systems are built upon a vast amount of data about users/items acquired by the services. Such knowledge represents an invaluable resource. However, commonly, part of this knowledge is public and can...
journal article 2022
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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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Isufi, E. (author), Pocchiari, Matteo (author), Hanjalic, A. (author)
Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns...
journal article 2021
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Pocchiari, M. (author)
Recommender Systems assist the user by suggesting items to be consumed based on the user's history. The topic of diversity in recommendation gained momentum in recent years as additional criterion besides recommendation accuracy, to improve user satisfaction. Accuracy and diversity in recommender systems coexist in a delicate trade-off due to...
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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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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Loni, B. (author)
Recommender Systems have become a crucial tool to serve personalized content and to promote online products and media, but also to recommend restaurants, events, news and dating profiles. The underlying algorithms have a significant impact on the quality of recommendations and have been the subject of many studies in the last two decades. In...
doctoral thesis 2018
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Kim, Jaehun (author), Won, Minz (author), Liem, C.C.S. (author), Hanjalic, A. (author)
In this paper, we propose a hybrid Neural Collaborative Filtering (NCF) model trained with a multi-objective function to achieve a music playlist generation system. The proposed approach focuses particularly on the cold-start problem (playlists with no seed tracks) and uses a text encoder employing a Recurrent Neural Network (RNN) to exploit...
conference paper 2018
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Jiao, Chongze (author)
Recent years, recommender systems are more and more important for solving information overload problem. They sort through massive data to provide users with personalized content and services. Most researchers focus on designing new algorithms to increase the performance of recommender systems. However, some open challenges stand: Why the...
master thesis 2017
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Arnason, B. (author)
The tremendous growth of the Internet brings with it a massive amount of data that users are exposed to on a daily basis. Consequently, information filtering techniques like recommender systems have become increasingly important to sift through the data and find what is relevant to a particular user. A recent approach for recommender systems,...
master thesis 2016
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Rentmeester, M. (author)
Most recommender systems recommend items from a single domain. However, usually users’ preferences span across multiple domains. Cross-domain recommender systems can successfully recommend items in multiple domains when there is knowledge about the user’s preferences for items in at least one of the domains and when there is knowledge about...
master thesis 2014
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Shi, Y. (author)
In this thesis we report the results of our research on recommender systems, which addresses some of the critical scientific challenges that still remain open in this domain. Collaborative filtering (CF) is the most common technique of predicting the interests of a user by collecting preference information from many users. In order to determine...
doctoral thesis 2013
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Oldenzeel, P.R. (author)
The increasing adoption of Enterprise Social Media (ESM) systems within enterprises is driven by the need for the explicit facilitation of sharing expertise. Expertise Identification (EI) functionality can satisfy this need. The social-media-like content and Collaborative Filtering (CF) annotation data available in ESM, however, pose unique...
master thesis 2012
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Onrust, B. (author), Verweijen, L.F. (author), Mandersloot, J. (author)
A group of three students worked a couple of months at CHAINels for their computer science bachelor project. In these months a recommendation algorithm was designed and implemented in CHAINels. The recommendation algorithm recommends posts to a company and those posts are shown in the Journal which was also made during this project. In this...
bachelor thesis 2012
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