Now showing items 21-28 of 28
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New Developer Metrics for Open Source Software Development Challenges: An Empirical Study of Project Recommendation Systems
(MDPI, 20.01.2021)Software collaboration platforms where millions of developers from diverse locations can contribute to the common open source projects have recently become popular. On these platforms, various information is obtained from ... -
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 ... -
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 ... -
Popularity bias in personality perspective: An analysis of how personality traits expose individuals to the unfair recommendation
(Wiley, Şubat, 202)Recommender systems are subject to well-known popularity bias issues, that is, they expose frequently rated items more in recommendation lists than less-rated ones. Such a problem could also have varying effects on users ... -
Robustness of privacy-preserving collaborative recommenders against popularity bias problem
(PeerJ, Temmuz, 20)Recommender systems have become increasingly important in today’s digital age, but they are not without their challenges. One of the most significant challenges is that users are not always willing to share their preferences ... -
The Unfairness of Collaborative Filtering Algorithms’ Bias Towards Blockbuster Items
(Ocak, 2023)It is known that collaborative filtering recommendation algorithms are usually biased towards some particular items (e.g., popular) in their produced ranked lists. In this study, we evaluate this problem from the perspective ... -
Treating adverse effects of blockbuster bias on beyond-accuracy quality of personalized recommendations
(Elsevier, 2022)Collaborative filtering recommendation algorithms are vulnerable against the popularity bias, including the most popular items repeatedly into the produced ranked lists. However, the research on popularity bias focuses ... -
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 ...
