Mitigating Popularity Bias in Recommendation via Counterfactual Inference

Popularity bias is a common problem in recommender systems. Existing research mainly tracks this problem by re-weighting training samples or leveraging a small fraction of unbiased data. However, the effect of popularity bias in user behavior data may lead to sacrifices in recommendation. In this paper, we exploit data bias from click behavior to derive popularity bias representation, and investigate how to mitigate its negative impact from a causal perspective. Motivated by causal effects, we propose a novel counterfactual inference framework named Mitigating Popularity Bias in Recommendation via Counterfactual Inference (MPCI), which enables us to capture the popularity bias as the direct causal effect of the prediction score, and we eliminate popularity bias by subtracting the direct popularity bias effect from the total causal effect. In this way, MPCI reduces popularity bias by decreasing the influence of popular items on model training. Extensive experiments on two real-world datasets demonstrate the superiority of our methods over some strong baselines and prove the effectiveness of mitigating popularity bias in recommender systems.