A Graph Convolution Neural Network for User-Group Aided Personalized Session-Based Recommendation

Session-based recommendation systems aim to predict the next user interaction based on the items with which the user interacts in the current session. Currently, graph neural network-based models have been widely used and proven more effective than others. However, these session-based models mainly focus on the user-item and item-item relations in historical sessions while ignoring information shared by similar users. To address the above issues, a new graph-based representation, User-item Group Graph, which considers not only user-item and item-item but also user-user relations, is developed to take advantage of natural sequential relations shared by similar users. A new personalized session-based recommendation model is developed based on this representation. It first generates groups according to user-related historical item sequences and then uses a user group preference recognition module to capture and balance between group-item preferences and user-item preferences. Comparison experiments show that the proposed model outperforms other state-of-art models when similar users are effectively grouped. This indicates that grouping similar users can help find deep preferences shared by users from the same group and is instructive in finding the most appropriate next item for the current user.