Complex Transitions Learning Model with Graph Attentive Network for Session-based Recommendation

Session-based recommendation aims to model users’ interests based on user sequences. Most models can’t fully capture user preferences. In this paper, we propose a Complex Transitions Learning model with Graph Attentive Network, which captures item transitions in neighbor sessions comprehensively. Global and local session graphs are constructed from multiple sessions. Item representations are learned by GNNs. Specially, representation of session and final item are combined to model association between items and sessions. Experimental results indicate that our method outperforms previous models.