Recommendation methods reduce popularity bias with new weighting scheme
Mitigating Popularity Bias in Recommendation with Global Listwise Learning and Progressive Bi-Weighting
Information Retrieval
Summary
Recommendation systems often show popular items too much because they learn from what users interact with most, which isn't always fair or diverse. The authors propose a new approach that looks at all items together and uses smarter ways to adjust how much each item counts during training. They also developed a method that starts by learning simple patterns before focusing on correcting popularity bias, which helps keep recommendations accurate. Tests on real data show their method works better than existing solutions.
What this means in practice
- •For ecommerce platform engineers: Improve product recommendation fairness by reducing popularity bias to promote diverse items.
- •For music streaming service developers: Enhance recommendation algorithms to better surface less popular tracks while maintaining accuracy.
Authors
Tianyu Zhu, Jiandong Ding, Yansong Shi, Guoqing Chen, Jian-Yun Nie
Abstract
In recommender systems, user feedback typically follows a long-tail distribution, which leads many recommendation algorithms to exacerbate popularity bias by disproportionately favoring popular items. To mitigate this issue, recent studies have employed Inverse Propensity Scoring (IPS) to rebalance training data via reweighting user-item interactions. However, the effectiveness of IPS-based approaches is often constrained by locally unbiased objectives and inaccurate propensity estimation. In this paper, we propose Multinomial Likelihood with Bi-Weighting (Mult-BiW) to address these limitations. First, we introduce a debiasing framework, termed Mult-IPS, which integrates multinomial likelihood with IPS to capture global and unbiased user preferences over the entire item set. Second, we develop a Bi-Weighting (BiW) strategy that jointly leverages propensity scores and a collection model, incorporating a smoothing mechanism to enhance the robustness of propensity estimation. We further provide theoretical analyses that establish an upper bound on the empirical bias and characterize the optimal form of the collection model. Third, to mitigate the adverse effects of aggressive reweighting on representation learning, we design a Progressive Bi-Weighting strategy that gradually transitions from discriminative representation learning to popularity debiasing. Extensive experiments on real-world datasets show that Mult-BiW consistently outperforms state-of-the-art baselines.