Debiasing Recommenders Through Personalized Popularity-Aware Margins

Abstract

Recommender systems based on Matrix Factorization are widely used. However, they can easily suffer from the problem of overrecommendation of popular items, i.e., popularity bias. To mitigate popularity bias, current methods often uniformly model interactions' popularity bias degree considering user activity and interacted item's popularity, and then force the recommenders to focus more on less biased interactions. However, users' popularity preference for candidate items also plays an important role in estimating popularity bias, which is ignored by current methods. Therefore, their uniform modeling of bias degree results in sub-optimal debiasing performance. To address this issue, our core idea is to estimate personalized bias degrees to perform user-specific debiasing. We first derive a predefined bias degree obtained by items' popularity, then scale it considering users' candidate items' popularity. Extensive experiments conducted on two classic MF-based recommenders and three real-world datasets demonstrate that our approach outperforms state-of-the-art methods for popularity debiasing.