Sequential Intention-aware Recommender based on User Interaction Graph

The next-item recommendation problem has received more and more attention from researchers in recent years. Ignoring the implicit item semantic information, existing algorithms focus more on the user-item binary relationship and suffer from high data sparsity. Inspired by the fact that user's decision-making process is often influenced by both intention and preference, this paper presents a SequentiAl inTentiOn-aware Recommender based on a user Interaction graph (Satori). In Satori, we first use a novel user interaction graph to construct relationships between users, items, and categories. Then, we leverage a graph attention network to extract auxiliary features on the graph and generate the three embeddings. Next, we adopt self-attention mechanism to model user intention and preference respectively which are later combined to form a hybrid user representation. Finally, the hybrid user representation and previously obtained item representation are both sent to the prediction modul to calculate the predicted item score. Testing on real-world datasets, the results prove that our approach outperforms state-of-the-art methods.