  Collaborative Filtering (CF) typically suffers from the significant challenge
of popularity bias due to the uneven distribution of items in real-world
datasets. This bias leads to a significant accuracy gap between popular and
unpopular items. It not only hinders accurate user preference understanding but
also exacerbates the Matthew effect in recommendation systems. To alleviate
popularity bias, existing efforts focus on emphasizing unpopular items or
separating the correlation between item representations and their popularity.
Despite the effectiveness, existing works still face two persistent challenges:
(1) how to extract common supervision signals from popular items to improve the
unpopular item representations, and (2) how to alleviate the representation
separation caused by popularity bias. In this work, we conduct an empirical
analysis of popularity bias and propose Popularity-Aware Alignment and Contrast
(PAAC) to address two challenges. Specifically, we use the common supervisory
signals modeled in popular item representations and propose a novel
popularity-aware supervised alignment module to learn unpopular item
representations. Additionally, we suggest re-weighting the contrastive learning
loss to mitigate the representation separation from a popularity-centric
perspective. Finally, we validate the effectiveness and rationale of PAAC in
mitigating popularity bias through extensive experiments on three real-world
datasets. Our code is available at
https://github.com/miaomiao-cai2/KDD2024-PAAC.
