Knowledge Graph Contrastive Learning for Recommendation

By Yuhao Yang, Chao Huang, Lianghao Xia, Chenliang Li. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022

506 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/895396389a9fee1b607bf5141a6cc7925bb1e069

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hongzhi Yin
2. Tong Chen
3. Xin Xia
4. Junliang Yu
5. Chao Huang
6. Lianghao Xia
7. Zi-Liang Huang
8. Jundong Li

In Information Systems

1. Hongzhi Yin
2. Tong Chen
3. Xin Xia
4. Junliang Yu
5. Chao Huang
6. Zi-Liang Huang
7. Lianghao Xia
8. Jundong Li

Citations in the corpus

• Self-Supervised Learning for Recommender Systems: A Survey (2022, methodology): “Besides, KGCL [88] applies dropout to knowledge graphs and proposes a knowledge-aware contrastive method which contrasts node representations learned from augmentations of the user-item graph and know graphs.”
• Hypergraph Contrastive Collaborative Filtering (2022, background): “It has achieved promising performance in various domains, such as visual data representation [5, 28], language data understanding [2, 31], graph representation learning [29, 51] and recommender systems [22, 38, 45, 46].”