Relevance-aware graph neural network for session-based recommendation

Session-based recommendation aims to predict the next item a user may interact with based on sessions' information. Most existing session-based recommendation models only rely on the information of item-to-item transitions. However, we argue that there exists another type of information in a session that can be mined and made use of, i.e., the information of item-to-item relevance, which could help increase a session-based recommendation model's performance. In this case, a model named Relevance-Aware Graph Neural Network (RA-GNN) is proposed in this paper, which captures and utilizes both types of information carried in a session, i.e., both the information of item-to-item transitions and the information of item-to-item relevance. Specifically, our paper first proposes to construct a relevance graph and transition graphs, respectively, to capture two types of information. Then two different embedding modules are designed to learn transition and relevance embeddings for items, respectively. Regarding the embeddings for sessions, to obtain the long-term and short-term interest of users, we propose a module to derive the global and local embeddings of sessions, which are further combined to obtain the session embeddings. Experiments are conducted on three real-world datasets to compare the proposed RA-GNN with the state-of-the-art session-based recommendation models. According to the results of experiments, our proposed RA-GNN outperforms the comparison models. The results of experiments show that the proposed model could effectively capture both types of sessions' information and obtain the interest of users more accurately, therefore improving the performance of session-based recommendation.