Countering Mainstream Bias via End-to-End Adaptive Local Learning
Jinhao Pan Ziwei Zhu Jianling Wang Allen Lin James Caverlee
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. Targeting
these causes, we propose a novel end-To-end Adaptive Local Learning (TALL)
framework to provide high-quality recommendations to both mainstream and niche
users. TALL uses a loss-driven Mixture-of-Experts module to adaptively ensemble
experts to provide customized local models for different users. Further, it
contains an adaptive weight module to synchronize the learning paces of
different users by dynamically adjusting weights in the loss. Extensive
experiments demonstrate the state-of-the-art performance of the proposed model.
Code and data are provided at
\url{https://github.com/JP-25/end-To-end-Adaptive-Local-Leanring-TALL-}