Improving Representation Learning for Session-based Recommendation
Tianwen Chen, Raymond Chi-Wing Wong · 2022 IEEE International Conference on Big Data (Big Data) · 2022
Session-based recommendation aims to predict the next item in an anonymous session. Recent advances have shown the importance of exploiting inter-session dependencies, such as item-item transitions and session-session similarities. However, the existing methods either ignore the relative order of item co-occurrences or assign the same importance to co-occurrence patterns at all distances. Besides, they are prone to extracting wrong signals to learn user preferences from dependencies between sessions. To solve these problems, we propose a model called FOCOL to better exploit the intersession dependencies by considering Fine-grained item co-Occurrences and applying the COntrastive Learning framework. Specifically, to capture inter-session item-item dependencies, we propose a component called FOGCN (Fine-grained co-Occurrence Graph Convolution Network) to automatically learn the importance of item co-occurrence patterns from a global graph that encodes the detailed information about item co-occurrences such as relative order and distance. To directly capture dependencies between sessions, we view the recommendation task as a clustering problem, and propose a component called CSRL (Contrastive Session Representation Learning) to implicitly group similar sessions (i.e., sessions with the same next item) into the same cluster and push apart sessions at different clusters. Extensive experiments conducted on three public datasets show that the proposed model is superior to the state-of-the-art methods and the proposed two components can learn more informative item and session representations by considering the fine-grained item co-occurrences and directly capturing dependencies between sessions.