Edge-view Dual Message Carriers GCN for Sequential Recommendation

Sequential Recommendation (SR) aims to predict the most likely coming user-item interaction based on their interaction history. Since bipartite graphs can easily represent SR records, GCN-based models proved to be very effective in SR. However, 1) directly importing high-order sequential patterns (SP) would improve performance but bring a huge complexity burden; 2) current GCNs are to learn nodes’ interaction characteristics in SR by transporting unary message carriers between adjacent nodes, which neglects that users and items are different. Thus, we proposed an Edge-view Dual message carriers GCN for sequential Recommendation (EDG4Rec), which regards users and items as dual heterogeneous message carriers delivering their influences on SR bipartite graph independently. Specifically, an interaction and its user’s 1-order SP and its item’s 1-order SP are joined directly as a basic domain, where the dual message carriers transported by our proposed DMT mechanism with three variants, i.e., CD-DMT, JSP-DMT, CI-DMT. Finally, an edge-based negative sampling mechanism (E-NSM) is proposed to make training faster. Extensive experiments on dense and sparse real-world datasets demonstrate the superior performance of our model. It shows that supplementary information is no longer necessary for our design. Besides, our model can work well in cold-start situations.