Causal embedding of user interest and conformity for long-tail session-based recommendations

Session-based recommendation is misleading by popularity bias and always favors short-head items with more popularity. This paper studies a new causal-based framework CauTailReS to increase the diversity of session recommendations. We first propose a new causal graph and then use the do-calculus in order to understand how popularity influences the process of making recommendations from the user's point of view. Popularity only misleads users temporarily, rather than in a long term and globally. Second, we believe that user clicks on popular products demonstrate their high quality and reputation. CauTailReS only eliminates 'bad' biases and retains 'good' effects through interest and consistent causal embedding mechanisms. To determine how similar various users are on various target items, CauTailReS also employs a re-ranking technique known as 'conformity-aware re-ranking'. To discover interactions based on what actual users want, CauTailReS also employs counterfactual reasoning. Extensive comparative experiments on four real world datasets have shown CauTailReS can well capture the true interests and consistency of users. As compared to the current state-of-the-art, CauTailReS enhances long-tail performance (APLT is increased by 8.14%) and recommendation accuracy (MRR is increased by 2.75%). This proves that introducing causal embeddings helps to reasonably enhance the diversity of recommendations.