Enhancing Session-Based Recommendation via Inter-Session Similar Intent Modeling and Graph Neural Networks

Session-based recommendation (SBR) is a challenging task that aims to make item recommendations based on anonymized user session data. Mainstream SBR efforts focus on modeling information within a session and do not use information from other sessions. Although some works try to use other session information, there are still many limitations, and how to model other session information is still a challenging task. To overcome these limitations, we propose a new method for learning similar intentions between sessions, aiming to better model the recommendation information contained in other sessions. Specifically, we contribute a new model named ISIM-GNN that learns and integrates three levels of information simultaneously: (i) In the intra-session representation learning layer, we represent the session as a session graph and model it using a gated graph neural network. (ii) In the global item embedding learning layer, we use the graph attention mechanism to propagate and aggregate relevant item information from other sessions on the global graph. (iii) In the inter-session similar intent learning layer, we employ both “hard similarity” and “soft similarity” to select similar sessions, and use the attention mechanism to conduct session-level aggregation on the selected similar sessions to make better use of the inter-session collaboration information. Experiments on three real-world datasets show a significant performance improvement of our approach compared to state-of-the-art work.