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.

翻译：协同过滤（CF）方法通常面临流行度偏差的显著挑战，这是由于现实世界数据集中物品分布不均所致。这种偏差导致热门物品与冷门物品之间存在显著的准确度差距。它不仅阻碍了对用户偏好的准确理解，还加剧了推荐系统中的马太效应。为缓解流行度偏差，现有研究主要侧重于强调冷门物品或分离物品表示与其流行度之间的关联。尽管这些方法有效，现有工作仍面临两个持续存在的挑战：（1）如何从热门物品中提取通用监督信号以改进冷门物品的表示学习；（2）如何缓解由流行度偏差引起的表示分离问题。本研究通过对流行度偏差进行实证分析，提出流行度感知对齐与对比（PAAC）方法以应对这两个挑战。具体而言，我们利用热门物品表示中建模的通用监督信号，提出一种新颖的流行度感知监督对齐模块来学习冷门物品的表示。此外，我们建议从以流行度为中心的视角重新加权对比学习损失，以缓解表示分离问题。最后，通过在三个真实数据集上的大量实验，我们验证了PAAC在缓解流行度偏差方面的有效性和合理性。我们的代码公开于 https://github.com/miaomiao-cai2/KDD2024-PAAC。