High-level Feature Fusion Network for Session-based Social Recommendation

The session-based social recommendation task aims to leverage knowledge from social networks to predict user actions based on their sessions. Most previous studies pay more attention to complex transitions to get item embedding in various ways and neglect the importance of users' role in social network. Therefore, we design a High-level Feature Fusion Network to address these issues. Firstly, to better leverage the knowledge from social networks, we use a heterogeneous graph neural network to enhance the user/item representation. Secondly, a user-based graph attention network is adapted to learn the user's deep interest evolution process. Next, user and session features are transferred to our feature fusion module to generate fusion features, and the original and fusion features are combined to make recommendations. Extensive experiments on three public datasets show that the proposed model outperforms existing state-of-the-art models.