Counterfactual Implicit Feedback Modeling
Haoxuan Li, Chuan Zhou, Lina Yao, Mingming Gong
In recommendation systems, implicit feedback data can be automatically recorded and is more common than explicit feedback data. However, implicit feedback poses two challenges for relevance prediction, namely (a) **positive-unlabeled**: negative feedback does not necessarily imply low relevance and (b) **missing not at random** (MNAR): items that are popular or frequently recommended tend to receive more clicks than other items, even if the user does not have a significant interest in them. Existing methods either ignore the MNAR problem or neglect the intrinsic positive-unlabeled causal mechanism of implicit feedback, which can face inaccurate relevance predictions or inflated biases and variance when modeling the exposure status. In this paper, we formulate the implicit feedback problem as a counterfactual estimation problem with missing treatment variables. Prediction of the relevance in implicit feedback is equivalent to answering the counterfactual question that "*whether a user would click a specific item if exposed to it?*". To solve the counterfactual question, we propose Counterfactual Implicit Feedback (Counter-IF) prediction approach that divides the user-item pairs into four disjoint groups, namely definitely positive (DP), highly exposed (HE), highly unexposed (HU) and unknown (UN) groups. Specifically, Counter-IF first performs missing treatment imputation with different confidence level from raw implicit feedback, then estimate the counterfactual outcomes via causal representation learning that combines pointwise loss and pairwise loss based on the user-item pairs stratification. We also theoretically derive the generalization bound of the learned model in terms of estimating the user true relevance. Extensive experiments are conducted on publicly available real-world datasets to demonstrate the effectiveness of our approach.