Trust-Aware Multi-Task Knowledge Graph for Recommendation

Data sparsity and cold start problems are common in recommender systems. Adding some side information, such as knowledge graph and users’ trust relationship, is an effective method to alleviate these problems. However, few work jointly explore the fine-grained implicit relationships between the external heterogeneous graphs to enhance the recommendation accuracy. To address this issue, in this paper, we propose a new method named Trust-aware Multi-task Knowledge Graph (TMKG), which uses multi-task learning to integrate two kinds of side information of trust graph and knowledge graph in an end-to-end manner. First, we mine the intra-graph and inter-graph high-order connections through the node propagation and aggregation, and optimize the embedding of nodes through the implicit relationships obtained. Furthermore, through the shared cross unit, the connection relationships between each layer is mined, and the high-order interaction of nodes of different layers is obtained. We conduct extensive experiments on real-world datasets and prove that our model has the superior performance compared with the state-of-the-art models.