Capturing Users’ Fresh Interests via Evolving Session-Based Social Recommendation

Abstract

Recommendation systems play a crucial part in helping users efficiently obtain information based on users’ current preferences and discover their individual needs, but the existing works are deficient in terms of the evolution of users’ interests. In this paper, Graph Embedding with Service and User information (GESU) model is proposed to address the limitations of capturing users’ fresh interests. Graph-structured data derived from time-varying session sequences are captured via gated graph neural networks. Then, the evolving influence of different services for users is obtained through a multi-head module. At the same time, a graph attention network is applied to predict users’ fresh consumption preferences by selecting representative friends to characterize user information. Extensive experiments on three datasets show that the proposed model outperforms state-of-the-art methods consistently on various evaluation metrics. GESU provides a means to recommend services that meet current requirements in an environment where users’ interests evolve dynamically.