Abstract: Abstract: Knowledge graph-based recommendation methods learn representations of user and item nodes by integrating relational connections from both knowledge graphs and user-item interaction graphs to recommend suitable items. However, since knowledge graphs contain both noisy and high-quality relations, the primary challenge lies in circumventing noisy relations while effectively mining valuable relations. Existing methods typically relied on global reconstruction strategies to optimize graph relations through either pruning noisy relations or mining high-quality relations in a single manner, thereby learning user and item representations. However, global perspectives struggled to sufficiently capture detailed local information and tended to overlook potential complementarity between local and global information. Furthermore, solely employing pruning or supplementation strategies failed to simultaneously mitigate interference from noisy relations and comprehensively exploit high-quality relations. To address these issues, a multi-perspective relation-enhanced knowledge graph recommendation method was proposed. This method alleviated noise impacts in knowledge graphs and user-item interaction graphs through an item hybrid relation alignment module and an interaction hybrid relation enhancement module, while deeply exploring valuable high-order relations. The item hybrid relation alignment module extracted local and global relations separately via importance pruning strategies and high-order relation mining approaches, followed by a knowledge alignment method to synergize both types of information for effectively refining high-quality item auxiliary information. The interaction hybrid relation enhancement module constructed local hybrid pruning relation graphs and global hybrid supplementation relation graphs, with cross-channel and cross-layer contrastive learning employed to enhance their informational complementarity, thereby comprehensively learning user and item representations. Finally, hierarchical gated adaptive fusion was adopted to integrate multiple sets of user and item embeddings for recommendation. Experimental results on multiple datasets demonstrated that compared with the VRKG4REC baseline model, the proposed model achieved improvements of 1.12% to 10.16% in NDCG metrics.

Key words: knowledge graph, multi-perspective, knowledge alignment, contrastive learning, gate fusion

摘要： 摘 要: 基于知识图谱的推荐方法通过结合物品属性图谱和用户交互图谱中的关系连接，学习用户和物品节点的表示，从而推荐合适的物品；然而，由于知识图谱同时包含噪声和优质关系，当前的主要挑战在于如何规避噪声关系并挖掘优质关系。现有方法通常基于全局的重构策略，通过剪裁噪声或挖掘优质关系的单一方式优化知识图谱关系，以此学习用户和物品的表示。然而，基于全局视角难以充分捕捉局部信息的细节，并且容易忽略局部信息与全局信息之间的潜在互补性；此外，仅依赖裁剪或增补策略难以同时规避噪声关系的干扰并全面挖掘优质关系。为了解决上述问题，提出一种基于多视角关系增强的知识图谱推荐方法。该方法通过物品混合关系对齐模块和交互混合关系增强模块减轻物品属性图和用户交互图中的噪声影响，同时深入挖掘优质高阶关系。物品混合关系对齐模块通过重要性裁剪策略和高阶关系挖掘方法分别提取局部与全局关系，并采用一种知识对齐的方法协同两类信息，有效提炼优质物品辅助信息；交互混合关系增强模块构建了局部混合裁剪关系图和全局混合增补关系图，并通过跨通道和跨层对比学习增强两者之间的信息互补性，从而全面学习用户和物品的表示。最后，采用层级门控自适应的方法融合多组用户与物品嵌入用于推荐。在多个数据集上的实验表明，相较于VRKG4REC基线模型，提出的模型在NDCG指标上提升了1.12%至10.16%。

关键词: 知识图谱, 多视角, 知识对齐, 对比学习, 门控融合