Abstract: Compared with traditional sequence modeling in session-based recommendation, modeling the session sequence as a graph structure performs better in this field. However, the existing research methods are limited in their ability to capture user’s current interest by only using the graph structure to mine the session characteristics between items. A current interest reinforced Graph neural network for session-based recommendation is proposed in the paper. By introducing position embedding and combining with graph neural network, the advantages of sequential perception model and graph perception model are complemented. The session sequence is modeled as a graph structure. Take the last click of the original sequence, and calculate its attention weight for graph node information through the multi-head attention mechanism, so as to obtain user’s current interest expression more accurately. Extensive experiments on two real-world datasets show that, the proposed method achieved the best performance of all methods, especially on the Diginetica data set, all evaluation indicators have increased by more than 7%, and the MRR@10 indicator has even increased by 9.52%. These results show the correctness and effectiveness of the proposed method for session-based recommendation.

Key words: session-based recommendation, session graph, graph neural network, multi-head attention, position embedding