Users’ trust relations have a significant influence on their choice towards different products. However, few recommendation or prediction algorithms both consider users’ social trust relations and item-related knowledge, which makes them difficult to cope with cold start and the data sparsity problems. In this paper, we propose a novel trust-ware recommendation method based on heterogeneous multi-relational graphs fusion, termed as T-MRGF. In contrast with other traditional methods, it fuses the user-related and item-related graphs with the user–item interaction graph and fully utilizes the high-level connections existing in heterogeneous graphs. Specifically, we first establish the user–user trust relation graph, user–item interaction graph and item–item knowledge graph, and the user feature and item feature, which have been obtained from the user–item graph, are used as the input of the user-related graph and the item-related graph respectively. The fusion is achieved through the cascade of feature vectors before and after feature propagation. In this way, the heterogeneous multi-relational graphs are fused for the feature propagation, which largely refines the user and item representation for model prediction. Simulation results show that the proposed method significantly improve the recommendation performance compared to the state-of-the-art KG-based algorithms both in accuracy and training efficiency.

用户的信任关系对他们选择不同产品有重大影响。但是，很少有推荐或预测算法同时考虑用户的社会信任关系和与项目相关的知识，这使得他们难以应对冷启动和数据稀疏性问题。在本文中，我们提出了一种基于异构多关系图融合的新型信任软件推荐方法，称为T-MRGF。与其他传统方法相比，它将用户相关图和项目相关图与用户-项目交互图融合在一起，并充分利用了异构图中存在的高级连接。具体来说，我们首先建立用户-用户信任关系图，用户-项目交互图和项目-项目知识图，以及用户特征和项目特征，从用户项目图获得的数据分别用作用户相关图和项目相关图的输入。通过特征传播之前和之后的特征向量的级联来实现融合。通过这种方式，融合了异构的多关系图以进行特征传播，从而极大地改善了用户和项目表示以进行模型预测。仿真结果表明，与最新的基于KG的算法相比，该方法在准确性和训练效率上均显着提高了推荐性能。融合了异构的多关系图以进行特征传播，这极大地改善了用户和项目表示的模型预测能力。仿真结果表明，与最新的基于KG的算法相比，该方法在准确性和训练效率上均显着提高了推荐性能。融合了异构的多关系图以进行特征传播，这极大地改善了用户和项目表示的模型预测能力。仿真结果表明，与最新的基于KG的算法相比，该方法在准确性和训练效率上均显着提高了推荐性能。