Causal Inference for Eliminating Popularity Bias in Session-based Recommendation

The general goal of the recommender system is to provide users with precise and personalized recommendations, rather than popular items. However, popularity bias widely exists in existing recommendation models, especially in Session-Based Recommendation(SBR) since the short-term users’ behaviors are much more susceptible to data skew and popularity. Unfortunately, existing SBR methods fail to explicitly take the popularity bias into consideration. Moreover, existing methods of eliminating popularity bias for sequence recommendation are not suitable for SBR scenarios where anonymous users’ information is hard to obtain. Thus, effective methods to eliminate popularity bias especially designed for SBR are needed. In this paper, we leverage causal inference theory to analyze the impact of item popularity by establishing a causal graph and then eliminating popularity bias through counterfactual reasoning. Specifically, we first construct a causal graph to describe the important causal relationship in the recommendation process and find that popularity bias is caused by the direct influence path from the item to the ranking score. Then we model the impact of causal relationships on the recommendation score through multi-task training. Finally, we eliminate the impact of item popularity by removing the direct impact of items according to counterfactual reasoning. We integrate the proposed popularity-eliminating method into the recently outstanding SBR model COTREC and conduct extensive experiments on two real datasets to validate that the casual inference to deal with popularity bias could further improve the recommendation accuracy and our model outperforms the SOTA SBR models consistently.