GNNRec: gated graph neural network for session-based social recommendation model

Abstract

Recommendation system utilizes user-item interactions and information on user’s attributes to infer the user’s interests and use them to make recommendations for the user. Graph neural network(GNN) has become more widely used in recommendation systems in recent years, because of their ability to naturally integrate node information and topology. However, most of the current recommendation methods based on graph structure only focus on a single recommendation domain (for example,session-based recommendation models or social recommendation models), without taking into account both the user’s behavior information and the user’s social relationships; Furthermore, session-based recommendation models usually use a recurrent neural network (RNN) to model user session, while RNN only focuses on the short-term impact of sessions and cannot cover all the information of sessions. Therefore, this paper proposes a novel session-based social recommendation model called GNNRec, which first utilizes gated graph neural network (GGNN) to represent users’ session information, and then uses graph attention network (GAT) to aggregate social information of users and friends on social networks to effectively model users’ interests. In this paper, experiments are conducted on two large datasets——Douban and Epinions, and the results show that the GNNRec model performs significantly better than current mainstream recommendation models.