  In dynamic interaction graphs, user-item interactions usually follow
heterogeneous patterns, represented by different structural information, such
as user-item co-occurrence, sequential information of user interactions and the
transition probabilities of item pairs. However, the existing methods cannot
simultaneously leverage all three structural information, resulting in
suboptimal performance. To this end, we propose TriSIM4Rec, a triple structural
information modeling method for accurate, explainable and interactive
recommendation on dynamic interaction graphs. Specifically, TriSIM4Rec consists
of 1) a dynamic ideal low-pass graph filter to dynamically mine co-occurrence
information in user-item interactions, which is implemented by incremental
singular value decomposition (SVD); 2) a parameter-free attention module to
capture sequential information of user interactions effectively and
efficiently; and 3) an item transition matrix to store the transition
probabilities of item pairs. Then, we fuse the predictions from the triple
structural information sources to obtain the final recommendation results. By
analyzing the relationship between the SVD-based and the recently emerging
graph signal processing (GSP)-based collaborative filtering methods, we find
that the essence of SVD is an ideal low-pass graph filter, so that the interest
vector space in TriSIM4Rec can be extended to achieve explainable and
interactive recommendation, making it possible for users to actively break
through the information cocoons. Experiments on six public datasets
demonstrated the effectiveness of TriSIM4Rec in accuracy, explainability and
interactivity.
