An open-source framework for self-supervised recommender systems.

Source code of paper "Extremely Simplified but Intent-enhanced Graph Collaborative Filtering for Recommendation"

[KDD 2024] Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning

Code for "Graph Contrastive Learning with Reinforcement Augmentation" (IJCAI 2024)

Diffusion-augmented Graph Contrastive Learning for Collaborative Filtering

The codes of `Unifying Graph Contrastive Learning via Graph Message Augmentation'

Dual-View Graph Contrastive Learning for Recommendation

Smoothed Graph Contrastive Learning

XSGCL: A Lightweight Graph Contrastive Learning Framework for Recommendation

CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations

AAAI 2025, Beyond Homophily: Graph Contrastive Learning with Macro-Micro Message Passing

Generative Data Augmentation in Graph Contrastive Learning for Recommendation (GDA4Rec)

SR-GCL

Recommender System Based on Noise Enhancement and Multi-view Graph Contrastive Learning

Attributed Graph Clustering with Multi-Scale Weight-Based Pairwise Coarsening and Contrastive Learning

The source code of Classification-wise and Cluster-wise Contrastive Learning for Graph Neural Recommendation

Multi-head graph contrastive learning with hop augmentation for node classification

Intelligent automation and multi-agent orchestration for Claude Code

[ACM TOMM'2025] "MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation"

NFGCL: A Negative-sampling-free Graph Contrastive Learning Framework for Recommendation