Popularity-aware sequential recommendation with user desire

In recent years, sequential recommendation system has attracted more and more attention from both academy and industry. Sequential recommendation treats the user’s historical behaviors as a sequence and captures the sequential dependency from it. Though the works on sequential recommendation have made great progress, sequential recommendation models still suffer from popularity bias. The existence of popularity bias is common in recommendation systems: the popular items are recommended overly while the less popular items which users may be interested in get fewer chances to be recommended than they should, which may negatively affect the recommendation accuracy and induce other problems such as Matthew Effect and Echo Chambers. Although there have been some works on alleviating popularity bias in traditional recommendation, there is still a lack of research on popularity bias in sequential recommendation. Because there exists complicated sequential dependency in sequences, the debiasing models designed for traditional recommendation cannot be applied directly, it is necessary to propose a debiasing model for sequential recommendation to take the complicated sequential dependency into account. To alleviate the popularity bias in sequential recommendation, we first propose a causal graph for sequential recommendation in which we propose to consider the dynamic user desire which would affect the effect of popularity. Then, we conduct intervention analysis and counterfactual reasoning to quantify the effect of popularity and reason about the user interaction probability in a hypothetical situation that the popularity is set to a certain value. Based on the results of obtained, we propose a new popularity-aware sequential recommendation model with user desire (PAUDRec) which consists of a Transformer-based module, a user desire module and a popularity effect module. Extensive experiments on four widely used benchmark datasets demonstrate that the proposed PAUDRec model outperforms the state-of-the-art sequential recommendation models while alleviating the popularity bias in sequential recommendation.