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Investigating and counteracting popularity bias in group recommendations
(Elsevier, 2021)
Popularity bias is an undesirable phenomenon associated with recommendation algorithms where popular items tend to be suggested over long-tail ones, even if the latter would be of reasonable interest for individuals. Such ...
Novel automatic group identification approaches for group recommendation
(Elsevier, 2021)
Group recommender systems are specialized in suggesting preferable products or services to a group of users rather than an individual by aggregating personal preferences of group members. In such expert systems, the initial ...
An entropy empowered hybridized aggregation technique for group recommender systems
(Elsevier, 15.03.2021)
Group recommender systems aim to suggest appropriate products/services to a group of users rather than individuals. These recommendations rely solely on determining group preferences, which is accomplished by an aggregation ...
Open Source Software Development Challenges: A Systematic Literature Review on GitHub
(IGI GLOBAL, 01.11.2021)
GitHub is the most common code hosting and repository service for open-source software (OSS) projects. Thanks to the great variety of features, researchers benefit from GitHub to solve a wide range of OSS development ...
Aggregating user preferences in group recommender systems: A crowdsourcing approach
(Elsevier, 2022)
We present that group recommendations are similar to crowdsourcing, where the responses of different crowd workers are aggregated in the absence of ground truth. With this in mind, we mimic the use of the EM algorithm as ...
Zero-shot learning via self-organizing maps
(Springer, 25.01.2023)
Collecting-labeled images from all possible classes related to the task at hand is highly impractical and may even be impossible. At this point, Zero-Shot Learning (ZSL) can enable the classification of new test classes ...