Disentangling interest and conformity for eliminating popularity bias in session-based recommendation

Session-based recommendation (SBR) is to predict the next item, given an anonymous interaction sequence. Recently, many advanced SBR models have shown great recommending performance, but few studies note that they suffer from popularity bias seriously: the model tends to recommend popular items and fails to recommend long-tail items. The only few debias works relieve popularity bias indeed. However, they ignore individual's conformity toward popular items and thus decrease recommending performance on popular items. Besides, conformity is always entangled with individual's real interest, which hinders extracting one's comprehensive preference. To tackle the problem, we propose an SBR framework with Disentangling InteRest and Conformity for eliminating popularity bias in SBR. In this framework, two groups of item encoders and session modeling modules are devised to extract interest and conformity, respectively, and a fusion module is designed to combine these two types of preference. Also, a discrepancy loss is utilized to disentangle the representation of interest and conformity. Besides, our devised framework can integrate with several SBR models seamlessly. We conduct extensive experiments on three real-world datasets with four advanced SBR models. The results show that our framework outperforms other state-of-the-art debias methods consistently.