Proxy-Aware Cross-Domain Sequential Recommendation

Cross-domain sequential recommendation (CDSR) aims to predict the next item that a user is most likely to interact with based on past sequential behavior from multiple domains. Existing works on CDSR usually transfer knowledge across different domains by linking items between domains via common users, which suffers from the following limitations: (1) due to the inherent differences between the domains, transferring information across domains can be affected by different representations of related items in different domains. (2) None of existing studies consider the time interval information among items, which is essential in sequential recommendation to capture user intents over time. In this work, we propose a novel cross-domain sequential recommendation model to address the above challenges. Specifically, we first design a shared proxy item encoder to generate a universal representation for each item in all domains by using its textual descriptions. Then, we develop a time-interval-aware attention encoder to represent sequences by considering the time interval information. Moreover, we present a contrastive learning auxiliary task to enhance a cross-domain sequence by weighing the importance of the items in the auxiliary domain with respect to the objective domain. Experiments demonstrate the superiority of our proposed method from various aspects.