From Deterministic to Probabilistic World: Balancing Enhanced Doubly Robust Learning for Debiased Recommendation

Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Min Zhang, Xu Chen, Xiao-Hua Zhou

Primary Area: general machine learning (i.e., none of the above)

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Keywords: Recommender system, Selection bias, Doubly robust, Probabilistic model

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TL;DR: This paper extend previous widely adopted doubly robust methods under deterministic error imputation and propensity models to be compatible with probabilistic error imputation and propensity models.

Abstract: In recommender systems, selection bias arises from the users' selective interactions with items, which poses a widely-recognized challenge for unbiased evaluation and learning for recommendation models. Recently, doubly robust and its variants have been widely studied to achieve debiased learning of prediction models, which enables unbiasedness when either imputed errors or learned propensities are accurate. However, we find that previous studies achieve unbiasedness using the doubly robust learning approaches are all based on deterministic error imputation model and deterministic propensity model, and these approaches fail to be unbiased when using probabilistic models to impute errors and learn propensities. To tackle this problem, in this paper, we first derive the bias of doubly robust learning methods and provide alternative unbiasedness conditions for probabilistic models. Then we propose a novel balancing enhanced doubly robust joint learning approach, which improves the accuracy of the imputed errors and leads to unbiased learning under probabilistic error imputations and learned propensities. We further derive the generalization error bound when using the probabilistic models, and show that it can be effectively controlled by the proposed learning approach. We conduct extensive experiments on three real-world datasets, including a large-scale industrial dataset, to demonstrate the effectiveness of the proposed method.

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Submission Number: 8839