Neural Collaborative Filtering for Social Recommendation Algorithm Based on Graph Attention

Abstract

The development of Internet technology has made the problem of information overload more and more serious.In order to solve the problems of data sparse and cold start of traditional recommendation technology,social recommendation has gradually become a research hotspot in recent years.As a network,graph neural networks(GNNs)can naturally integrate node information and topology,offer great potential for improving social recommendation.But there are still many challenges for social recommendation based on graph neural network.For example,how to learn accurate latent factor representations of users and items from user-item interaction graphs and social network graphs;Simply mapping of inherent properties of users and items to obtain embeddings,but key collaborative signals of user-item interactions are not learned.In order to learn more accurate latent factor representations,capture key collaborative signals,and improve the performance of recommender systems,a graph attention-based neural collaborative filtering social recommendation model(AGNN-SR) is proposed.The model is based on user-item interaction graphs and social network graphs,and learns latent factors of users and items from multiple perspectives through a multi-head attention mechanism.In addition,graph neural networks utilize higher-order connectivity to recursively propagate embedding information on the graph,explicitly encoding collaborative signaling to explore deep and complex interactions between users and items.Finally,the effectiveness of the AGNN-SR model is verified on three real datasets.

Keywords

• social recommendation|graph neural network|multi-head attention|neural collaborative filtering