Causally Debiased Time-aware Recommendation

Lei Wang, Chen Ma, Xian Wu, Zhaopeng Qiu, Yefeng Zheng, Xu Chen

Keywords: Time-aware recommendation, Collaborative filtering, Counterfactual, Causal inference

Abstract:

Time-aware recommendation has been widely studied for modeling the user dynamic preference and a lot of models have
been proposed. However, these models often overlook the fact that users may not behave evenly on the timeline, and observed datasets
can be biased by user intrinsic preferences or previous recommender systems, leading to degraded model performance. We propose
a causally debiased time-aware recommender framework to accurately learn user preference. We formulate the task of time-aware
recommendation by a causal graph, identifying two types of biases on the item and time levels. To optimize the ideal unbiased learning
objective, we propose a debiased framework based on the inverse propensity score (IPS) and extend it to the doubly robust method.
Considering that the user preference can be diverse and complex, which may result in unmeasured confounders, we develop a sensitivity
analysis method to obtain more accurate IPS. We theoretically draw a connection between the proposed method and the ideal learning
objective, which to the best of our knowledge, is the first time in the research community. We conduct extensive experiments on three
real-world datasets to demonstrate the effectiveness of our model. To promote this research direction, we have released our project at https://www-cdtr.github.io/.

Track: User Modeling and Recommendation

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Submission Number: 495