Session Recommendation Algorithm Combining Item Transition Relations and Time-Order Information

Abstract

Aiming at the problem that the existing graph neural network session recommendation algorithm ignores all kinds of auxiliary information, which leads to the inability to accurately model the session sequence, a session recommendation algorithm combining the item transition relations and time-order information (RTSR) is proposed. Firstly, the shortest path sequence between any two nodes is obtained by using the graph network structure, which is encoded as the item transition relations between corresponding items through the gated recurrent unit (GRU), and then the global dependency information of the session is captured from the perspective of the graph by combining the self-attention mechanism. At the same time, a lossless graph coding scheme is designed to alleviate the problem of information loss in the process of session graph coding. The scheme quantifies the time-order information in the session sequence reasonably, and takes it as the weight of the edges in the session graph, and then combines the gated graph sequence neural network to obtain the local dependency information of the session. Finally, with linear combination of global dependency information and local dependency information, and in combination with reverse position information,  the user??s preference for item is finally generated, and the recommendation list is given. The performance comparison experiment with mainstream models such as SR-GNN, GC-SAN and GCE-GNN on the public benchmark datasets Gowalla and Diginetica shows that RTSR  improves at least 6.13% and 1.58% in average reciprocal ranking respectively, and the recommendation accuracy is also improved accordingly.