Meta-optimized Session-based Recommendation with Adaptive Multi-level User Intent Integration

Zhenchao Wu, Hongteng Xu, Xu Chen

Abstract: Session-based recommendation aims to predict the next item in the absence of user profiles and extensive interactions. Recent recommender techniques, such as attention mechanisms and graph neural networks, have shown promising results. However, most existing methods treat each item within a session as an independent intent unit, thus focusing solely on single-level user interests. This strategy overlooks the presence of multiple granularity levels of user intent that may exist within a session. To address this, we bundle locally consecutive items together as session snippets, which are modeled as high-level user intent units in the form of set-based and sequence-based representations. The user intent units are further enriched by aggregating multi-granularity neighbor information through two types of representations in the Heterogeneous Session Graph. We then treat the last intent unit from each guanularity as a query vector, learning the corresponding session representation using an attention mechanism. Based on this, we calculate a separate recommendation result. Next, we fine-tune the pre-trained meta-weight network to customize the weights for the recommendation results at different granularities. These weights are then used to fuse the results and generate the final recommendation. Extensive experiments on three real-world datasets demonstrate that our approach outperforms state-of-the-art methods.

External IDs:dblp:conf/ijcnn/WuXC25