# Knowledge Graph Contrastive Learning for Recommendation

arXiv: https://arxiv.org/abs/2205.00976 · HTML: https://mlsanity.com/paper/2205.00976/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 55 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 30 of 36)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning](https://mlsanity.com/paper/2004.11660/) (2020)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [Knowledge Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1904.12575/) (2019)
- [Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://mlsanity.com/paper/1906.05237/) (2019)
- [Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://mlsanity.com/paper/2101.06448/) (2021)
- [Learning Intents behind Interactions with Knowledge Graph for Recommendation](https://mlsanity.com/paper/2102.07057/) (2021)
- [Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences](https://mlsanity.com/paper/1902.06236/) (2019)
- [Towards Domain-Agnostic Contrastive Learning](https://mlsanity.com/paper/2011.04419/) (2020)
- [Improving Question Answering by Commonsense-Based Pre-Training](https://mlsanity.com/paper/1809.03568/) (2018)
- [Contrastive Self-supervised Sequential Recommendation with Robust Augmentation](https://mlsanity.com/paper/2108.06479/) (2021)
- [Knowledge-aware Coupled Graph Neural Network for Social Recommendation](https://mlsanity.com/paper/2110.03987/) (2021)
- [Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2110.04000/) (2021)
- [Graph Meta Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2110.03969/) (2021)
- [Contrastive Meta Learning with Behavior Multiplicity for Recommendation](https://mlsanity.com/paper/2202.08523/) (2022)
- [LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding](https://mlsanity.com/paper/2012.07335/) (2020)
- [Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation](https://mlsanity.com/paper/1904.12796/) (2019)

## Built on by (most cited first, 30 of 70)

- [Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure](https://mlsanity.com/paper/2211.10738/) (2022)
- [Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation](https://mlsanity.com/paper/2207.05584/) (2022)
- [DiffKG: Knowledge Graph Diffusion Model for Recommendation](https://mlsanity.com/paper/2312.16890/) (2023)
- [Self-Supervised Hypergraph Transformer for Recommender Systems](https://mlsanity.com/paper/2207.14338/) (2022)
- [Heterogeneous Contrastive Learning for Foundation Models and Beyond](https://mlsanity.com/paper/2404.00225/) (2024)
- [Towards Graph Contrastive Learning: A Survey and Beyond](https://mlsanity.com/paper/2405.11868/) (2024)
- [An Overview on Language Models: Recent Developments and Outlook](https://mlsanity.com/paper/2303.05759/) (2023)
- [Knowledge Graph Self-Supervised Rationalization for Recommendation](https://mlsanity.com/paper/2307.02759/) (2023)
- [SSLRec: A Self-Supervised Learning Framework for Recommendation](https://mlsanity.com/paper/2308.05697/) (2023)
- [Knowledge Enhancement for Contrastive Multi-Behavior Recommendation](https://mlsanity.com/paper/2301.05403/) (2023)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion](https://mlsanity.com/paper/2208.07622/) (2022)
- [A Directional Diffusion Graph Transformer for Recommendation](https://mlsanity.com/paper/2404.03326/) (2024)
- [Comprehending Knowledge Graphs with Large Language Models for Recommender Systems](https://mlsanity.com/paper/2410.12229/) (2024)
- [HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcare](https://mlsanity.com/paper/2507.19726/) (2025)
- [Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks](https://mlsanity.com/paper/2405.13085/) (2024)
- [Symmetric Graph Contrastive Learning against Noisy Views for Recommendation](https://mlsanity.com/paper/2408.02691/) (2024)
- [Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation](https://mlsanity.com/paper/2402.15988/) (2024)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Unsupervised Pretraining for Fact Verification by Language Model Distillation](https://mlsanity.com/paper/2309.16540/) (2023)
- [Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction](https://mlsanity.com/paper/2312.06682/) (2023)
- [Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis](https://mlsanity.com/paper/2308.07942/) (2023)
- [UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential Recommendations](https://mlsanity.com/paper/2406.18470/) (2024)
- [RecKG: Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/2501.03598/) (2025)
- [Multi-Behavior Recommender Systems: A Survey](https://mlsanity.com/paper/2503.06963/) (2025)
- [HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding](https://mlsanity.com/paper/2305.10563/) (2023)
- [Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis](https://mlsanity.com/paper/2308.00404/) (2023)
- [KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion](https://mlsanity.com/paper/2404.03893/) (2024)
- [Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs](https://mlsanity.com/paper/2501.01644/) (2025)
- [Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation](https://mlsanity.com/paper/2304.07506/) (2023)

## Abstract (arXiv)

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