A Spatiotemporal Graph Neural Network for session-based recommendation

Session-based recommendation is a challenging task dedicating to predict the user's next item of interest based on a given sequence of anonymous behaviors. Most existing methods aim to model items' temporal and spatial relationships, including recurrent neural network-based and graph neural network-based approaches. However, they have stagnated, expressed inadequacy, and rarely considered user behavior patterns hidden behind items that were common in the click process. In this paper, we propose a Spatiotemporal Graph Neural Network (SGNN) model for the session-based recommendation, which can effectively simulate the users' behavior patterns from a spatiotemporal perspective to determine the next click. The proposed SGNN model mainly consists of two modules: a Spatiotemporal Session Graph (SSG) module and a Preference-aware AttentioN (PAN) module. In the SSG module, to simulate the possible behavior patterns of users, we model all session sequences as directed and weighted sequences. Then, these session sequences are integrated into a spatial graph, followed by an auto-regressive moving average convolution filter, thus availably assessing the time-decay of user preference. In the PAN module, considering that each item is not equally important to the user's next click on the item, first of all, we divide the session into global behavioral interest and current interest, where the global behavioral interests fully capture the overall preferences in the session. Furthermore, we use the last clicked item in the session as the current interest to enhance the prediction of the next click behavior. Extensive experiments on two public datasets show that the proposed SGNN model consistently outperforms the comparative methods interns of accuracy and performance.