Modeling Cross-session Information with Multi-interest Graph Neural Networks for the Next-item Recommendation
Ting-Yun Wang, Chiao-Ting Chen, Ju-Chun Huang, Szu-Hao Huang
Abstract
Next-item recommendation involves predicting the next item of interest of a given user from their past behavior. Users tend to browse and purchase various items on e-commerce websites according to their varied interests and needs, as reflected in their purchasing history. Most existing next-item recommendation methods aim at extracting the main point of interest in each browsing session and encapsulate it in a single representation. However, past behavior sequences reflect the multiple interests of a single user, which cannot be captured by methods that focus on single-interest contexts. Indeed, multiple interests cannot be captured in a single representation, and doing so results in missing information. Therefore, we propose a model with a multi-interest structure for capturing the various interests of users from their behavior sequence. Moreover, we adopted a method based on a graph neural network to construct interest graphs based on the historical and current behavior sequences of users. These graphs can capture complex item transition patterns related to different interests. In experiments, the proposed method outperforms state-of-the-art session-based recommendation systems on three real-world datasets, achieving 4% improvement of Recall over the SOTAs on Jdata dataset.