Rethinking Contrastive Learning for Graph Collaborative Filtering: Limitations and a Simple Remedy
Fuente:
arXiv
Saved in:
| Main Authors: | Lee, Geon, Kim, Sunwoo, Kim, Kyungho, Shin, Kijung |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation
by: Kim, Kyungho, et al.
Published: (2025)
by: Kim, Kyungho, et al.
Published: (2025)
Multi-Behavior Recommender Systems: A Survey
by: Kim, Kyungho, et al.
Published: (2025)
by: Kim, Kyungho, et al.
Published: (2025)
ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation
by: Kim, Sunwoo, et al.
Published: (2025)
by: Kim, Sunwoo, et al.
Published: (2025)
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy
by: Kim, Sunwoo, et al.
Published: (2024)
by: Kim, Sunwoo, et al.
Published: (2024)
From Raw Features to Effective Embeddings: A Three-Stage Approach for Multimodal Recipe Recommendation
by: Shin, Jeeho, et al.
Published: (2025)
by: Shin, Jeeho, et al.
Published: (2025)
Personalized Parameter-Efficient Fine-Tuning of Foundation Models for Multimodal Recommendation
by: Kim, Sunwoo, et al.
Published: (2026)
by: Kim, Sunwoo, et al.
Published: (2026)
KGMEL: Knowledge Graph-Enhanced Multimodal Entity Linking
by: Kim, Juyeon, et al.
Published: (2025)
by: Kim, Juyeon, et al.
Published: (2025)
Hybrid-Vector Retrieval for Visually Rich Documents: Combining Single-Vector Efficiency and Multi-Vector Accuracy
by: Kim, Juyeon, et al.
Published: (2025)
by: Kim, Juyeon, et al.
Published: (2025)
Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering
by: Wu, Yihong, et al.
Published: (2024)
by: Wu, Yihong, et al.
Published: (2024)
L^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering
by: Jin, Xinzhou, et al.
Published: (2024)
by: Jin, Xinzhou, et al.
Published: (2024)
Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering
by: Chen, Zheyu, et al.
Published: (2025)
by: Chen, Zheyu, et al.
Published: (2025)
Diffusion-augmented Graph Contrastive Learning for Collaborative Filter
by: Huang, Fan, et al.
Published: (2025)
by: Huang, Fan, et al.
Published: (2025)
TwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering
by: Liu, Chengkai, et al.
Published: (2024)
by: Liu, Chengkai, et al.
Published: (2024)
Self-supervised Contrastive Learning for Implicit Collaborative Filtering
by: Song, Shipeng, et al.
Published: (2024)
by: Song, Shipeng, et al.
Published: (2024)
Intent Propagation Contrastive Collaborative Filtering
by: Li, Haojie, et al.
Published: (2026)
by: Li, Haojie, et al.
Published: (2026)
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
by: Zhang, Yu, et al.
Published: (2025)
by: Zhang, Yu, et al.
Published: (2025)
C$^3$: Capturing Consensus with Contrastive Learning in Group Recommendation
by: Kim, Soyoung, et al.
Published: (2025)
by: Kim, Soyoung, et al.
Published: (2025)
Disentangled Contrastive Collaborative Filtering
by: Ren, Xubin, et al.
Published: (2023)
by: Ren, Xubin, et al.
Published: (2023)
Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
by: Sun, Peijie, et al.
Published: (2024)
by: Sun, Peijie, et al.
Published: (2024)
Towards Unified and Adaptive Cross-Domain Collaborative Filtering via Graph Signal Processing
by: Lee, Jeongeun, et al.
Published: (2024)
by: Lee, Jeongeun, et al.
Published: (2024)
Neural Causal Graph Collaborative Filtering
by: Wang, Xiangmeng, et al.
Published: (2023)
by: Wang, Xiangmeng, et al.
Published: (2023)
Feedback Reciprocal Graph Collaborative Filtering
by: Chen, Weijun, et al.
Published: (2024)
by: Chen, Weijun, et al.
Published: (2024)
Lightweight Embeddings for Graph Collaborative Filtering
by: Liang, Xurong, et al.
Published: (2024)
by: Liang, Xurong, et al.
Published: (2024)
SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning
by: Lee, Jongha, et al.
Published: (2024)
by: Lee, Jongha, et al.
Published: (2024)
ASPIRE: Make Spectral Graph Collaborative Filtering Great Again via Adaptive Filter Learning
by: He, Yunhang, et al.
Published: (2026)
by: He, Yunhang, et al.
Published: (2026)
SVD-AE: Simple Autoencoders for Collaborative Filtering
by: Hong, Seoyoung, et al.
Published: (2024)
by: Hong, Seoyoung, et al.
Published: (2024)
PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering
by: Qin, Yifang, et al.
Published: (2024)
by: Qin, Yifang, et al.
Published: (2024)
Lightweight Embeddings with Graph Rewiring for Collaborative Filtering
by: Liang, Xurong, et al.
Published: (2025)
by: Liang, Xurong, et al.
Published: (2025)
CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems
by: Park, Jin-Duk, et al.
Published: (2024)
by: Park, Jin-Duk, et al.
Published: (2024)
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
by: Kim, Chanwoo, et al.
Published: (2025)
by: Kim, Chanwoo, et al.
Published: (2025)
QAGCF: Graph Collaborative Filtering for Q&A Recommendation
by: Zhang, Changshuo, et al.
Published: (2024)
by: Zhang, Changshuo, et al.
Published: (2024)
Graph Neural Controlled Differential Equations For Collaborative Filtering
by: Xu, Ke, et al.
Published: (2025)
by: Xu, Ke, et al.
Published: (2025)
Light distillation for Incremental Graph Convolution Collaborative Filtering
by: Fan, X, et al.
Published: (2025)
by: Fan, X, et al.
Published: (2025)
Rethinking Contrastive Learning in Session-based Recommendation
by: Zhang, Xiaokun, et al.
Published: (2025)
by: Zhang, Xiaokun, et al.
Published: (2025)
Mean-Variance Efficient Collaborative Filtering for Stock Recommendation
by: Chung, Munki, et al.
Published: (2023)
by: Chung, Munki, et al.
Published: (2023)
Learning Binarized Representations with Pseudo-positive Sample Enhancement for Efficient Graph Collaborative Filtering
by: Chen, Yankai, et al.
Published: (2025)
by: Chen, Yankai, et al.
Published: (2025)
A Gated Hybrid Contrastive Collaborative Filtering Recommendation
by: da Silva, Eduardo Ferreira, et al.
Published: (2026)
by: da Silva, Eduardo Ferreira, et al.
Published: (2026)
General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout
by: Zhang, An, et al.
Published: (2024)
by: Zhang, An, et al.
Published: (2024)
Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation
by: Xu, Jinfeng, et al.
Published: (2024)
by: Xu, Jinfeng, et al.
Published: (2024)
Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty
by: Li, Haoxuan, et al.
Published: (2024)
by: Li, Haoxuan, et al.
Published: (2024)
Similar Items
-
A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation
by: Kim, Kyungho, et al.
Published: (2025) -
Multi-Behavior Recommender Systems: A Survey
by: Kim, Kyungho, et al.
Published: (2025) -
ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendation
by: Kim, Sunwoo, et al.
Published: (2025) -
Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy
by: Kim, Sunwoo, et al.
Published: (2024) -
From Raw Features to Effective Embeddings: A Three-Stage Approach for Multimodal Recipe Recommendation
by: Shin, Jeeho, et al.
Published: (2025)