[PDF][PDF] FAIRNESS IN SEQUENTIAL GROUP RECOMMENDATIONS
MM Hasan - 2024 - trepo.tuni.fi
FAIRNESS IN SEQUENTIAL GROUP RECOMMENDATIONS Page 1 Md. Mahade Hasan
FAIRNESS IN SEQUENTIAL GROUP RECOMMENDATIONS Faculty of Information Technology …
FAIRNESS IN SEQUENTIAL GROUP RECOMMENDATIONS Faculty of Information Technology …
Utilizing Implicit Feedback for User Mainstreaminess Evaluation and Bias Detection in Recommender Systems
K Zhang, M Xie, Y Zhang, H Zhang - … on Algorithmic Bias in Search and …, 2023 - Springer
Bias and fairness issues have attracted considerable attention in recommender systems.
From the user's perspective, intentions to stay or leave heavily depend on the degree of …
From the user's perspective, intentions to stay or leave heavily depend on the degree of …
Raising Teenagers' Awareness of Social Media Threats: A Theoretical and Empirical Study
F Lomonaco - 2023 - boa.unimib.it
Social media are pervasive in our daily lives. These platforms are gaining momentum thanks
to powerful artificial intelligence models. Serious negative consequences of social media …
to powerful artificial intelligence models. Serious negative consequences of social media …
Group Recommendations with Responsibility Constraints
M Stratigi - 2023 - trepo.tuni.fi
The expansion of social media has led more people to form groups for specific activities,
and, consecutively, group recommender systems have emerged as popular research. In …
and, consecutively, group recommender systems have emerged as popular research. In …
[HTML][HTML] Reinforcement learning page prediction for hierarchically ordered municipal websites
P Puustinen, K Stefanidis, J Kekäläinen, M Junkkari - Information, 2021 - mdpi.com
Public websites offer information on a variety of topics and services and are accessed by
users with varying skills to browse the kind of electronic document repositories. However …
users with varying skills to browse the kind of electronic document repositories. However …
[PDF][PDF] Unbiasing collaborative filtering for popularity-aware recommendation
We analyze the behavior of recommender systems relative to the popularity of the items to
recommend. Our findings show that most popular ranking-based recommenders are biased …
recommend. Our findings show that most popular ranking-based recommenders are biased …