Mixed Information Flow for Cross-domain Sequential Recommendations

Cross-domain sequential recommendation (CDSR) is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in CDSR is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of behavioral information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this paper, we propose a mixed information flow network (MIFN) for CDSR to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior transfer unit (BTU) and a knowledge transfer unit (KTU). MIFN is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users' current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that MIFN is able to further improve recommendation performance in different domains by modeling mixed information flow.