Knowledge Graphs (KGs) have been utilized as useful side information to
improve recommendation quality. In those recommender systems, knowledge graph
information often contains fruitful facts and inherent semantic relatedness
among items. However, the success of such methods relies on the high quality
knowledge graphs, and may not learn quality representations with two
challenges: i) The long-tail distribution of entities results in sparse
supervision signals for KG-enhanced item representation; ii) Real-world
knowledge graphs are often noisy and contain topic-irrelevant connections
between items and entities. Such KG sparsity and noise make the item-entity
dependent relations deviate from reflecting their true characteristics, which
significantly amplifies the noise effect and hinders the accurate
representation of user's preference.
To fill this research gap, we design a general Knowledge Graph Contrastive
Learning framework (KGCL) that alleviates the information noise for knowledge
graph-enhanced recommender systems. Specifically, we propose a knowledge graph
augmentation schema to suppress KG noise in information aggregation, and derive
more robust knowledge-aware representations for items. In addition, we exploit
additional supervision signals from the KG augmentation process to guide a
cross-view contrastive learning paradigm, giving a greater role to unbiased
user-item interactions in gradient descent and further suppressing the noise.
Extensive experiments on three public datasets demonstrate the consistent
superiority of our KGCL over state-of-the-art techniques. KGCL also achieves
strong performance in recommendation scenarios with sparse user-item
interactions, long-tail and noisy KG entities. Our implementation codes are
available at this https URL

提出了一种知识图谱对比学习的框架 (KGCL)，该框架通过知识图谱增强模式减少了噪声，利用交叉视图对比学习技术进一步抑制了噪声，实现了在稀疏、长尾和有噪声的知识图谱场景下的强大性能。