Cluster Anchor Regularization to Alleviate Popularity Bias in Recommender Systems

Recommender systems are essential for finding personalized content for users on online platforms. These systems are often trained on historical user interaction data, which collects user feedback on system recommendations. This creates a feedback loop leading to popularity bias; popular content is over-represented in the data, better learned, and thus recommended even more. Less popular content struggles to reach its potential audiences. Popularity bias limits the diversity of content that users are exposed to, and makes it harder for new creators to gain traction. Existing methods to alleviate popularity bias tend to trade off the performance of popular items. In this work, we propose a new method for alleviating popularity bias in recommender systems, called the cluster anchor regularization, which partitions the large item corpus into hierarchical clusters, and then leverages the cluster information of each item to facilitate transfer learning from head items to tail items. Our results demonstrate the effectiveness of the proposed method with offline analyses and live experiments on a large-scale industrial recommendation platform, where it significantly increases tail recommendation without hurting the overall user experience.