Abstract
Recommender systems usually face the bias problem, which creates a gap between recommendation results and the actual user preference. Existing works track this problem by assuming a specific bias and then develop a method to mitigate it, which lack universality. In this paper, we attribute the root reason of the bias problem to a causality concept: confounders, which are the variables that influence both which items the user will interact with and how they rate them. Meanwhile, the theory around causality says that some confounders may remain unobserved and are hard to calculate. Accordingly, we propose a novel Counterfactual Inference for Deconfounded Recommendation (CIDR) framework that enables the analysis of causes of biases from a causal perspective. We firstly analyze the causal-effect of confounders, and then utilize the biased observational data to capture a substitute of confounders on both user side and item side. Finally, we boost counterfactual inference to eliminate the causal-effect of such confounders in order to achieve a satisfactory recommendation with the help of user and item side information (e.g., user post-click feedback data, item multi-model data). For evaluation, we compare our method with several state-of-the-art debias methods on three real-world datasets, in addition to new causal-based approaches. Extensive experiments demonstrate the effectiveness of our proposed method.
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He, M., Hu, X., Li, C., Chen, X., Wang, J. (2023). Mitigating Confounding Bias for Recommendation via Counterfactual Inference. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13713. Springer, Cham. https://doi.org/10.1007/978-3-031-26387-3_32
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DOI: https://doi.org/10.1007/978-3-031-26387-3_32
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