Graph Collaborative Filtering

Graph Collaborative Filtering (GCF) leverages graph neural networks to model user-item interactions for improved recommendation systems, aiming to capture complex relationships and address data sparsity. Current research focuses on enhancing GCF's performance through contrastive learning techniques, exploring various methods for constructing informative contrastive pairs and mitigating issues like sampling bias and unwanted noise from graph augmentations. These advancements aim to improve recommendation accuracy and fairness, impacting both the theoretical understanding of GNNs in recommender systems and the practical development of more effective and equitable recommendation algorithms.

Papers

August 22, 2024

Fair Augmentation for Graph Collaborative Filtering
Ludovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, Giacomo Medda
Collaborative Filtering User Item Fair Graph Graph Collaborative Filtering

July 19, 2024

L^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering
Xinzhou Jin, Jintang Li, Liang Chen, Chenyun Yu, Yuanzhen Xie, Tao Xie, Chengxiang Zhuo, Zang Li, Zibin Zheng
Contrastive Learning Collaborative Filtering Graph Augmentation Graph Collaborative Filtering

February 3, 2024

Prototypical Contrastive Learning through Alignment and Uniformity for Recommendation
Yangxun Ou, Lei Chen, Fenglin Pan, Yupeng Wu
Effective Recommendation Alignment Problem Uniformity Metric Prototypical Contrastive Learning Contrastive Pair Graph Collaborative Filtering

August 1, 2023

Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Tommaso Di Noia, Eugenio Di Sciascio
Graph Based Graph Model Recommendation Accuracy Traditional Recommender Reproducibility Study Many Myth Graph Collaborative Filtering

July 18, 2023

Sharpness-Aware Graph Collaborative Filtering
Huiyuan Chen, Chin-Chia Michael Yeh, Yujie Fan, Yan Zheng, Junpeng Wang, Vivian Lai, Mahashweta Das, Hao Yang
Graph Neural Network Non Convex Global Minimum Graph Collaborative Filtering

June 6, 2023

On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
Jiayan Guo, Lun Du, Xu Chen, Xiaojun Ma, Qiang Fu, Shi Han, Dongmei Zhang, Yan Zhang
Recommender System Collaborative Filtering Graph Filter User Item Graph Collaborative Filtering Based Collaborative Filtering

April 5, 2022

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
Jun Hu, Bryan Hooi, Shengsheng Qian, Quan Fang, Changsheng Xu
Graph Neural Network GNN Based Graph Diffusion Network Representation Item Embeddings Neural Collaborative Filtering Graph Collaborative Filtering

February 13, 2022

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
Zihan Lin, Changxin Tian, Yupeng Hou, Wayne Xin Zhao
Contrastive Learning Contrastive Objective Contrastive Pair Graph Collaborative Filtering

Graph Collaborative Filtering

Papers

Fair Augmentation for Graph Collaborative Filtering

L^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering

Prototypical Contrastive Learning through Alignment and Uniformity for Recommendation

Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis

Sharpness-Aware Graph Collaborative Filtering

On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning