Knowledge Graph Contrastive Learning for Recommendation

TL;DR

This paper introduces KGCL, a contrastive learning framework that enhances knowledge graph-based recommendations by reducing noise and sparsity, leading to more accurate user preference modeling especially in noisy or sparse data scenarios.

Contribution

The paper proposes a novel KG contrastive learning framework with a KG augmentation schema and cross-view contrastive learning to improve recommendation quality under noisy and sparse KG conditions.

Findings

KGCL outperforms state-of-the-art methods on three datasets.

It is effective in scenarios with sparse interactions and noisy KGs.

The approach enhances robustness of item representations.

Abstract

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 https://github.com/yuh-yang/KGCL-SIGIR22