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Showing 1 to 20 of 134 for “"recommender systems"”.
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Modelling Group Recommender Systems
… to systematically study how to model group for recommender systems with different kinds of auxiliary group information. 1) Social-aware recommendation approaches assume that the knowledge in social user-user connections can be shared and transferred to the domain of user-item interactions, …
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Towards decentralized recommender systems
Automated recommender systems make product suggestions that are tailored <br>to the human user's individual needs and represent powerful means to combat <br>information glut. However, their practical applicability has been largely confined to scenarios where all information relevant for …
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Uncertainty in Recommender Systems
Recommender Systems have emerged as a powerful tool in the information era. Due to the overwhelming number of items (products and services) currently offered on digital platforms, it is often necessary to use a system capable of ranking the items and offering those that are most relevant to each …
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Designing Sustainable Recommender Systems
Recommender systems are widely deployed to serve users with content they like. However, content must be created and insufficient demand dampens a creator’s production incentive. We argue that the canonical recommender system may not be sustainable if, by promoting the content each user likes the …
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Active caching for recommender systems
… while carrying out browsing and searching tasks. Recommender systems substantially reduce the information overload by suggesting a list of similar documents that users might find interesting. However, generating these ranked lists requires an enormous amount of resources that often results in …
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Trust networks for recommender systems
Recommender systems use information about their user’s profiles and relationships to suggest items that might be of interest to them. Recommenders that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional systems, …
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Recommender systems for manual testing
A atividade de teste de software pode ser bastante árdua e custosa. No contexto de testes manuais, todo o esforço com o objetivo de reduzir o tempo de execução dos testes e aumentar a contenção de defeitos é bem-vindo. Uma possível estratégia é alocar os casos de teste de acordo com o perfil do …
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Enhanced Recommender Systems by Biclustering
Recommender systems play a very important role to explore and suggest personalized recommendations to users from a huge number of choices. In this thesis, we propose a new class of enhanced recommender systems, called BiRDS (meaning Biclustering Recommendation Systems) and develop three different …
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Demystifying graph neural networks in recommender systems
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-04-06 without embargo terms
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Kibitz : a framework for creating recommender systems
Recommender systems are one of the most vital and ubiquitous parts of the modern web. They are used by many major internet services such as Facebook, Google, and Amazon. However, there is a wealth of content and data that remains untapped by mainstream commercial recommender systems. We have …
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Accurate and Trustworthy Recommender Systems: Algorithms and Findings
… which has been addressed through the use of recommender systems. Modern recommender systems use deep learning algorithms trained with user-item interaction data to generate recommendations. However, current recommender systems still face diverse challenges with respect to accuracy, …
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Automatic Knowledge Acquisition for Critiquing-Based Recommender Systems
Many recommender systems rely heavily on user ratings and historical data to produce recommendations. When lacking such data, these systems do not perform well. Knowledge-based recommender systems on the other hand are ideal for naive and casual users. However, the challenge for these systems lies …
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Aspect-based sentiment analysis for social recommender systems.
Social recommender systems harness knowledge from social content, experiences and interactions to provide recommendations to users. The retrieval and ranking of products, using similarity knowledge, is central to the recommendation architecture. To enhance recommendation performance, having an …
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Learning-based Attack and Defense on Recommender Systems
… is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. On the other hand, many spammers write deceptive reviews to change the credibility of …
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Use of discrete choice models with recommender systems
Recommender systems, also known as personalization systems, are a popular technique for reducing information overload and finding items that are of interest to the user. Increasingly, people are turning to these systems to help them find the information that is most valuable to them. A variety of …
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Evaluating, Understanding, and Mitigating Unfairness in Recommender Systems
Recommender systems are information filtering tools that discover potential matchings between users and items and benefit both parties. This benefit can be considered a social resource that should be equitably allocated across users and items, especially in critical domains such as education and …
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Recommender Systems for the Conference Paper Assignment Problem
… We study this problem as an application of recommender systems research. Besides the traditional goal of predicting `who likes what?', a conference management system must take into account reviewer capacity constraints, adequate numbers of reviews for papers, expertise modeling, conflicts of …
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Recommender systems and market approaches for industrial data management
… relevance or costs of providing these datasets. Recommender systems and so-called market approaches have previously been used to solve this type of resource allocation problem, as shown for example in allocation of equipment for production processes in manufacturing or for spare part supplier …
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Challenges in recommender systems : scalability, privacy, and structured recommendations
In this thesis, we tackle three challenges in recommender systems (RS): scalability, privacy and structured recommendations. We first develop a scalable primal dual algorithm for matrix completion based on trace norm regularization. The regularization problem is solved via a constraint generation …
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Jumping Connections: A Graph-Theoretic Model for Recommender Systems
Recommender systems have become paramount to customize information access and reduce information overload. They serve multiple uses, ranging from suggesting products and artifacts (to consumers), to bringing people together by the connections induced by (similar) reactions to products and services. …
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