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Countering Mainstream Bias via End-to-End Adaptive Local Learning
April 16, 2024, 4:43 a.m. | Jinhao Pan, Ziwei Zhu, Jianling Wang, Allen Lin, James Caverlee
cs.LG updates on arXiv.org arxiv.org
Abstract: Collaborative filtering (CF) based recommendations suffer from mainstream bias -- where mainstream users are favored over niche users, leading to poor recommendation quality for many long-tail users. In this paper, we identify two root causes of this mainstream bias: (i) discrepancy modeling, whereby CF algorithms focus on modeling mainstream users while neglecting niche users with unique preferences; and (ii) unsynchronized learning, where niche users require more training epochs than mainstream users to reach peak performance. …
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