Real-time social recommendation based on graph embedding and temporal context

Abstract With the rapid proliferation of online social networks, personalized social recommendation has become an important means to help people discover their potential friends or interested items in real-time. However, the cold-start issue and the special properties of social networks, such as rich temporal dynamics, heterogeneous and complex structures, render the most commonly used recommendation approaches (e.g. Collaborative Filtering) inefficient. In this paper, we propose a novel dynamic graph-based embedding (DGE) model for social recommendation which is capable of recommending relevant users and interested items. In order to support real-time recommendation, we construct a heterogeneous user-item (HUI) network and incrementally maintain it as the social network evolves. DGE jointly captures the temporal semantic effects, social relationships and user behavior sequential patterns in a unified way by embedding the HUI network into a shared low dimensional space. Then, with simple search methods or similarity calculations, we can use the encoded representation of temporal contexts to generate recommendations. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets, and the experimental results show its advantages over other state-of-the-art methods.