Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias

TL;DR

This paper introduces PAAC, a novel method that uses popularity-aware alignment and contrastive learning to reduce popularity bias in collaborative filtering, improving recommendation fairness and accuracy for unpopular items.

Contribution

The paper proposes a new approach combining supervised alignment and re-weighted contrastive learning to better mitigate popularity bias in recommendation systems.

Findings

PAAC effectively reduces popularity bias in experiments.

Improves recommendation accuracy for unpopular items.

Validated on three real-world datasets.

Abstract

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.