TGNRec: Recommendation Based on Trust Networks and Graph Neural Networks

In recent years, user-user trust relationships have played an important role in recommendation based on graph neural networks(GNNs). However, existing studies based on GNNs still face the following challenges: how to obtain more rating information of users’ trust from trust networks when using GNNs to learn the user latent feature. And how to effectively mine items’ relationships from the recommended data so that GNNs can better learn the item latent feature. To address the above challenges, in this paper, we propose a new model called TGNRec that accomplishes recommendation based on trust networks and graph neural networks. TGNRec consists of three modules: User Spatial Module, Item Spatial Module, Prediction Module. User Spatial Module considers both the rating information of users’ direct and indirect trust based on the transfer properties of trust relationships in trust networks. It mainly learns the user latent feature using user-item interactions and user-user trust relationships. Item Spatial Module establishes items’ similarity relationships based on the rating mean, which helps GNNs learn the item latent feature from user-item interactions and item-item relationships. Prediction Module realizes users’ rating prediction for unrated items by aggregating User Spatial Module and Item Spatial Module. At last, we conduct experiments on two real-world datasets, Film Trust and Ciao-DVD. The experimental results demonstrate the effectiveness of TGNRec for rating prediction in recommendation.