Mitigating Confounding and Selection Biases in Personalized Recommendation: A Causal Approach.

Recommender systems usually face confounding bias and selection bias. The former arises when hidden variables determine user/item features and an outcome variable simultaneously while the latter happens due to some biased selection mechanisms, e.g., choosing users based on a specific time or location. How to alleviate such biases has attracted a lot research attention in recent years, but existing approaches mainly focus on one specific source of bias, rather than handle both confounding and selection biases. To this end, we formulate the causal personalized recommendation problem based on the structural causal model (SCM) and a generalization of the notion of backdoor adjustment to account for both biases. Our approach leverages external data of some variables that are also measured without selection bias and uses an adjustment pair based on the derived graphical conditions for identifying conditional causal effects. We present a statistical estimation procedure based on inverse probability weighting to calculate conditional causal effects when training samples are limited. In the presence of confounding and selection biases, we also show how to derive path-specific effects and counterfactual effects, both of which are important for recommendation analysis. We demonstrate the effectiveness of our approach through empirical evaluations.