新研究解决推荐系统中的公平性和可靠性问题

报道来源 [4]

1. arXiv cs.AI TIER_1 English(EN) · Yanan Wang, Yong Ge · 2026-06-30 04:00

   Recommender Systems上的公平性攻击

   arXiv:2606.29064v1 Announce Type: cross Abstract: The unfairness of recommender systems has become a topic of concern due to its significant social and ethical implications. Although existing works have shown the effectiveness of attacks on the performance of recommender systems …

2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Eadan Schechter · 2026-06-28 11:16

   推荐系统中的单义性

   Latent factor models such as matrix factorization are widely used in recommender systems, yet the learned embedding dimensions typically lack explicit semantic interpretation. This opacity limits transparency, explainability, and principled intervention in recommendation behavior…

3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Yong Ge · 2026-06-27 19:50

   Recommender Systems 的公平性攻击

   The unfairness of recommender systems has become a topic of concern due to its significant social and ethical implications. Although existing works have shown the effectiveness of attacks on the performance of recommender systems (e.g., promotion and demotion attack), the study o…

4. arXiv stat.ML TIER_1 English(EN) · Aurore Archimbaud, Andreas Alfons, Ines Wilms · 2026-06-29 04:00

   迈向可靠的评分数据推荐系统

   arXiv:2412.20802v3 Announce Type: replace Abstract: Recommender systems are widely used in the digital landscape to match users with content fitting their preferences. However, growing concerns about fake accounts, strategic manipulation, and other deceptive online behavior place…