Improved Global and Local Graph Neural Network for Session-based Recommendation

Abstract

The session-based recommendation(SBR) aims to predict user actions based on anonymous sessions. SBR focuses on recent sessions to make more real-time and reliable recommendations and does not need to extract user IDs, thereby alleviating privacy issues. Recent research mainly models user preferences based on the target session while ignoring other sessions, which may contain items transitions related to and unrelated to the target session. This paper proposes a novel method, namely Improved Global and Local Graph Neural Network (IGL-GNN), to models item transitions within not only the target session but also the other sessions. Specifically, we learn the session-level item representation from the local graph, learns the global-level item representation from the global graph. We propose a new method to effectively aggregate global and local representations, which not only retains more useful information but also removes useless information. In addition, we innovatively introduced Transformer, incorporated it into the model as a general deformation function, enhanced the ability to obtain complex transformations, and solved the limitations of the model in learning complex representations. We have done a lot of experiments on three benchmark datasets, and the results show that the performance of IGL-GNN is better than the most advanced methods.