Context-aware Session-based Recommendation with Graph Neural Networks
This work improves recommendation systems for users in e-commerce or content platforms by providing more accurate session-based predictions, though it is incremental as it builds on existing graph neural network approaches.
The paper tackled the problem of session-based recommendation by addressing limitations in existing methods, such as failing to distinguish item-item edge types and using fixed item embeddings, and proposed CARES, a context-aware model with graph neural networks that outperformed state-of-the-art models on three benchmark datasets, achieving improvements in P@20 and MRR@20.
Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session. While there are methods that leverage rich context information in sessions for SBR, most of them have the following limitations: 1) they fail to distinguish the item-item edge types when constructing the global graph for exploiting cross-session contexts; 2) they learn a fixed embedding vector for each item, which lacks the flexibility to reflect the variation of user interests across sessions; 3) they generally use the one-hot encoded vector of the target item as the hard label to predict, thus failing to capture the true user preference. To solve these issues, we propose CARES, a novel context-aware session-based recommendation model with graph neural networks, which utilizes different types of contexts in sessions to capture user interests. Specifically, we first construct a multi-relation cross-session graph to connect items according to intra- and cross-session item-level contexts. Further, to encode the variation of user interests, we design personalized item representations. Finally, we employ a label collaboration strategy for generating soft user preference distribution as labels. Experiments on three benchmark datasets demonstrate that CARES consistently outperforms state-of-the-art models in terms of P@20 and MRR@20. Our data and codes are publicly available at https://github.com/brilliantZhang/CARES.