Knowledge Graph Contrastive Learning for Recommendation

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

This paper has been published as a full paper at SIGIR 2022

References in corpus (15)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• KGAT: Knowledge Graph Attention Network for Recommendation
• Graph Convolutional Matrix Completion
• Knowledge Graph Convolutional Networks for Recommender Systems
• Graph Contrastive Learning with Augmentations
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• Graph Meta Network for Multi-Behavior Recommendation
• Contrastive Meta Learning with Behavior Multiplicity for Recommendation
• DKN: Deep Knowledge-Aware Network for News Recommendation
• Social Recommendation with Self-Supervised Metagraph Informax Network
• Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
• MVIN: Learning Multiview Items for Recommendation
• Recent Advances in Heterogeneous Relation Learning for Recommendation
• Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

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• Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure
• Automated Self-Supervised Learning for Recommendation
• Knowledge Enhancement for Contrastive Multi-Behavior Recommendation
• Multi-Behavior Graph Neural Networks for Recommender System
• SSLRec: A Self-Supervised Learning Framework for Recommendation
• Knowledge-refined Denoising Network for Robust Recommendation
• Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction
• Knowledge Enhanced Multi-intent Transformer Network for Recommendation
• A Comprehensive Survey on Self-Supervised Learning for Recommendation
• Comprehending Knowledge Graphs with Large Language Models for Recommender Systems
• Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning
• Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
• LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
• RecKG: Knowledge Graph for Recommender Systems
• Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning
• KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?
• Reproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message Passing
• Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph
• SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation
• Generative Data Augmentation in Graph Contrastive Learning for Recommendation