  Cross-domain sequential recommendation 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 cross-domain sequential
recommendation 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 for cross-domain
sequential recommendation to consider both the flow of behavioral information
and the flow of knowledge by incorporating a behavior transfer unit and a
knowledge transfer unit. The proposed mixed information flow network 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 mixed information flow network is
able to further improve recommendation performance in different domains by
modeling mixed information flow.
