# Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

arXiv: https://arxiv.org/abs/2204.08807 · HTML: https://mlsanity.com/paper/2204.08807/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2022.

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

- [DiffKG: Knowledge Graph Diffusion Model for Recommendation](https://mlsanity.com/paper/2312.16890/) (2023)
- [Knowledge Graph Self-Supervised Rationalization for Recommendation](https://mlsanity.com/paper/2307.02759/) (2023)
- [Improving Personality Consistency in Conversation by Persona Extending](https://mlsanity.com/paper/2208.10816/) (2022)
- [A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer](https://mlsanity.com/paper/2402.02464/) (2024)
- [STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2211.15003/) (2022)
- [Contrastive Learning on Medical Intents for Sequential Prescription Recommendation](https://mlsanity.com/paper/2408.10259/) (2024)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning](https://mlsanity.com/paper/2502.06101/) (2025)
- [A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation](https://mlsanity.com/paper/2310.03747/) (2023)
- [RecKG: Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/2501.03598/) (2025)
- [Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue](https://mlsanity.com/paper/2210.08801/) (2022)
- [HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold](https://mlsanity.com/paper/2210.08806/) (2022)
- [Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation](https://mlsanity.com/paper/2304.07506/) (2023)
- [LogicRec: Recommendation with Users' Logical Requirements](https://mlsanity.com/paper/2304.11722/) (2023)
- [Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs](https://mlsanity.com/paper/2310.12008/) (2023)
- [Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework](https://mlsanity.com/paper/2311.13864/) (2023)
- [FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling](https://mlsanity.com/paper/2403.02630/) (2024)
- [HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation](https://mlsanity.com/paper/2404.09848/) (2024)
- [MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2504.16576/) (2025)
- [Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendation](https://mlsanity.com/paper/2505.08157/) (2025)
- [IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation](https://mlsanity.com/paper/2308.15926/) (2023)
- [TIGER: Temporally Improved Graph Entity Linker](https://mlsanity.com/paper/2410.09128/) (2024)
- [LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture](https://mlsanity.com/paper/2506.10347/) (2025)
- [Enhancing Bandit Algorithms with LLMs for Time-varying User Preferences in Streaming Recommendations](https://mlsanity.com/paper/2602.08067/) (2026)
- [On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation](https://mlsanity.com/paper/2309.13384/) (2023)
- [Simple Multigraph Convolution Networks](https://mlsanity.com/paper/2403.05014/) (2024)
- [Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation](https://mlsanity.com/paper/2404.11993/) (2024)
- [Reproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message Passing](https://mlsanity.com/paper/2503.07823/) (2025)
- [DCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation](https://mlsanity.com/paper/2605.07314/) (2026)
- [Multi-Level Graph Attention Network Contrastive Learning for Knowledge-Aware Recommendation](https://mlsanity.com/paper/2605.08499/) (2026)

## Abstract (arXiv)

Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which generate two graph views by uniform data augmentation schemes such as corruption or dropping, we comprehensively consider three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views. Specifically, we consider the user-item graph as a collaborative view, the item-entity graph as a semantic view, and the user-item-entity graph as a structural view. MCCLK hence performs contrastive learning across three views on both local and global levels, mining comprehensive graph feature and structure information in a self-supervised manner. Besides, in semantic view, a k-Nearest-Neighbor (kNN) item-item semantic graph construction module is proposed, to capture the important item-item semantic relation which is usually ignored by previous work. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/CCIIPLab/MCCLK.