PIDKG: Propagating Interaction Influence on the Dynamic Knowledge Graph for Recommendation

Modelling the dynamic interactions between users and items on knowledge graphs is very crucial for improving the accuracy of recommendation. Although existing methods have made great progress in modelling the dynamic knowledge graphs for recommendation, they usually only consider the mutual influence between users and items involved in the interactions, and ignore the influence propagation from the interacting nodes (i.e., users and items) on dynamic knowledge graphs. In this paper, we propose an influence propagation-enhanced deep coevolutionary method for recommendation, which can capture not only the direct mutual influence between interacting users and items but also influence propagation from multiple interacting nodes to their high-order neighbors at the same time on the dynamic knowledge graph. Specifically, the proposed model consists of two main components: direct mutual influence component and influence propagation component. The former captures direct interaction influence between the interacting users and items to generate the effective representations for them. The latter refines their representations via aggregating the interaction influence propagated from multiple interacting nodes. In this process, a neighbor selection mechanism is designed for selecting more effective propagation influence, which can significantly reduce the computational cost and accelerate the training. Finally, the refined representations of users and items are used to predict which item the user is most likely to interact with. The experimental results on three real-world datasets illustrate that the effectiveness and robustness of PIDKG outperform all the state-of-the-art baselines and the efficiency of it is faster than most of comparative baselines.