Recommender systems often suffer from popularity bias, where popular items are overly recommended while sacrificing unpopular items. Existing researches generally focus on ensuring the number of recommendations exposure of each item is equal or proportional, using inverse propensity weighting, causal intervention, or adversarial training. However, increasing the exposure of unpopular items may not bring more clicks or interactions, resulting in skewed benefits and failing in achieving real reasonable popularity debiasing. In this paper, we propose a new criterion for popularity debiasing, i.e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion. Under the guidance of the criterion, we then propose a debiasing framework with IPL regularization term which is theoretically shown to achieve a win-win situation of both popularity debiasing and recommendation performance. Experiments conducted on four public datasets demonstrate that when equipping two representative collaborative filtering models with our framework, the popularity bias is effectively alleviated while maintaining the recommendation performance.

翻译：推荐系统常受流行度偏差困扰，即热门物品被过度推荐而冷门物品被牺牲。现有研究通常通过逆倾向加权、因果干预或对抗训练等方法，确保各物品的推荐曝光次数均等或成比例。然而，增加冷门物品的曝光未必能带来更多点击或交互，导致收益不均，未能实现真正合理的流行度去偏。本文提出流行度去偏的新准则——在无偏推荐系统中，热门与冷门物品应获得与其喜爱用户数量成比例的交互，即IPL准则。在此准则指导下，我们提出带有IPL正则化项的去偏框架，理论上可证明该框架能实现流行度去偏与推荐性能的双赢。在四个公开数据集上的实验表明，将两个代表性协同过滤模型嵌入该框架后，在维持推荐性能的同时有效缓解了流行度偏差。