Abstract
Implicit feedback (e.g., user clicks) is widely used in building recommender systems (RS). However, the inherent notorious exposure bias significantly affects recommendation performance. Exposure bias refers a phenomenon that implicit feedback is influenced by user exposure and does not precisely reflect user preference. Current methods for addressing exposure bias primarily reduce confidence in unclicked data, employ exposure models, or leverage propensity scores. Regrettably, these approaches often lead to biased estimations or elevated model variance, yielding sub-optimal results. To overcome these limitations, we propose a new method ReCRec that Reasons the Causes behind the implicit feedback for debiased Recommendation. ReCRec identifies three scenarios behind unclicked data—i.e., unexposed, dislike, or a combination of both. A reasoning module is employed to infer the category to which each instance pertains. Consequently, the model is capable of extracting reliable positive and negative signals from unclicked data, thereby facilitating more accurate learning of user preferences. We also conduct thorough theoretical analyses to demonstrate the debiased nature and low variance of ReCRec. Extensive experiments on both semi-synthetic and real-world datasets validate its superiority over state-of-the-art methods.
Siyi Lin
Student
Siyi Lin is currently a Master student in ZLST, where he is supervised by Prof.Can Wang and Profs.Jiawei Chen.
Jiawei Chen
陈佳伟 研究员
Yan Feng
冯雁 副教授
Qihao Shi
史麒豪 副研究员
Chun Chen
陈纯 院士
