Mitigating Confounding Bias in Practical Recommender Systems With Partially Inaccessible Exposure Status

Authors:
raw_author_name: Tianwei Cao
raw_affiliation_string: School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Qianqian Xu
raw_affiliation_string: Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
raw_author_name: Zhiyong Yang
raw_affiliation_string: School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Qingming Huang
raw_affiliation_string: Key Laboratory of Big Data Mining and Knowledge Management (BDKM), University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Peng Cheng Laboratory, Shenzhen, China; School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

Abstract: To improve user experience, recommender systems have been widely used on many online platforms. In these systems, recommendation models are typically learned from positive/negative feedback that are collected automatically. Notably, recommender systems are a little different from general supervised learning tasks. In recommender systems, there are some factors (e.g. previous recommendation models or operation strategies of a online platform) that determine which items can be exposed to each individual user. Normally, the previous exposure results are not only relevant to the instances' features (i.e., user or item), but also affect their feedback ratings, thus leading to confounding bias in the recommendation models. To mitigate this bias, researchers have already provided a variety of strategies. However, there are still two issues that are underappreciated: 1) previous debiased RS approaches cannot effectively capture recommendation-specific, exposure-specific and their common knowledge simultaneously; 2) the true exposure results of the user-item pairs are partially inaccessible, so there would be some noises if we use their observability to approximate it as existing approaches. Motivated by this, we develop a novel debiasing recommendation approach. More specifically, we first propose a mutual information-based counterfactual learning framework based on the causal relationship among the instance features, exposure status, and ratings. This framework can 1) capture recommendation-specific, exposure-specific and their common knowledge by explicitly modeling the relationship among the causal factors, and 2) achieve robustness towards partially inaccessible exposure results by a pairwise learning strategy. Under such a framework, we implement an optimizable loss function with theoretical analysis. By minimizing this loss, we expect to obtain an unbiased recommendation model that reflects the users' real interests. Meanwhile, we also prove that our loss function has robustness towards the partial inaccessibility of the exposure status. Finally, extensive experiments on public datasets manifest the superiority of our proposed method in boosting the recommendation performance.