A heterogeneous graph neural recommendation model with hierarchical social trust

In traditional social recommendation algorithms, methods based on Graph Neural Networks (GNNs) representation of social networks are widely used, but the increasing sparsity of data and the tendency of users' social influence to be complex make it difficult for the algorithms to improve the recommendation quality efficiently. To address the issues, a heterogeneous graph neural recommendation model with hierarchical social trust (GraphNON) is proposed. The algorithm is improved by fully exploiting user-friend relationships and modeling different propagation channels of social influence in multiple ways respectively. Firstly, different types of potential friends are mined based on user-user social graph and user-item rating graph respectively. Different dynamic influences of direct friends and indirectly friends among users are modeled through hierarchical influence propagation. Secondly, associations between items are mined from user-item rating graph, and influence transmission among items is modeled through hierarchical influence propagation. Finally, updated user characteristics and item characteristics are used to predict whether a user is interested in an item or not. It is shown on both datasets that the model has significant improvement in both hit rate (HR) and normalized discounted cumulative gain (NDCG) metrics compared to the optimal benchmark model. For a potential feature dimension of 64, the HR of the Yelp dataset improved by 18.3% and the NDCG by 29.3%, while the HR of the Flickr dataset improved by 31.4% and the NDCG by 37.1%.