Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Weizhi, Yang, Liangwei, Song, Zihe, Zou, Henry Peng, Xu, Ke, Fang, Liancheng, Yu, Philip S. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mixed Supervised Graph Contrastive Learning for Recommendation
by: Zhang, Weizhi, et al.
Published: (2024)
by: Zhang, Weizhi, et al.
Published: (2024)
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
by: Zhang, Weizhi, et al.
Published: (2025)
by: Zhang, Weizhi, et al.
Published: (2025)
Graph Neural Controlled Differential Equations For Collaborative Filtering
by: Xu, Ke, et al.
Published: (2025)
by: Xu, Ke, et al.
Published: (2025)
Training Large Recommendation Models via Graph-Language Token Alignment
by: Yang, Mingdai, et al.
Published: (2025)
by: Yang, Mingdai, et al.
Published: (2025)
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?
by: Malitesta, Daniele, et al.
Published: (2024)
by: Malitesta, Daniele, et al.
Published: (2024)
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
by: Zou, Henry Peng, et al.
Published: (2024)
by: Zou, Henry Peng, et al.
Published: (2024)
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
by: Zhang, Weizhi, et al.
Published: (2025)
by: Zhang, Weizhi, et al.
Published: (2025)
Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search
by: Attimonelli, Matteo, et al.
Published: (2025)
by: Attimonelli, Matteo, et al.
Published: (2025)
Knowledge Graph Context-Enhanced Diversified Recommendation
by: Liu, Xiaolong, et al.
Published: (2023)
by: Liu, Xiaolong, et al.
Published: (2023)
Item Cluster-aware Prompt Learning for Session-based Recommendation
by: Yang, Wooseong, et al.
Published: (2024)
by: Yang, Wooseong, et al.
Published: (2024)
Personalized Multi-task Training for Recommender System
by: Yang, Liangwei, et al.
Published: (2024)
by: Yang, Liangwei, et al.
Published: (2024)
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
by: Cao, Yuwei, et al.
Published: (2024)
by: Cao, Yuwei, et al.
Published: (2024)
Learning Graph ODE for Continuous-Time Sequential Recommendation
by: Qin, Yifang, et al.
Published: (2023)
by: Qin, Yifang, et al.
Published: (2023)
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation
by: Wang, Xinhua, et al.
Published: (2023)
by: Wang, Xinhua, et al.
Published: (2023)
SynerGraph: An Integrated Graph Convolution Network for Multimodal Recommendation
by: Burabak, Mert, et al.
Published: (2024)
by: Burabak, Mert, et al.
Published: (2024)
Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems
by: Fan, Dongzhe, et al.
Published: (2026)
by: Fan, Dongzhe, et al.
Published: (2026)
Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering
by: Wu, Yihong, et al.
Published: (2024)
by: Wu, Yihong, et al.
Published: (2024)
Are Large Language Models Really Effective for Training-Free Cold-Start Recommendation?
by: Kusano, Genki, et al.
Published: (2025)
by: Kusano, Genki, et al.
Published: (2025)
Post-Training Attribute Unlearning in Recommender Systems
by: Chen, Chaochao, et al.
Published: (2024)
by: Chen, Chaochao, et al.
Published: (2024)
The Limits of Graph Samplers for Training Inductive Recommender Systems: Extended results
by: Jendal, Theis E., et al.
Published: (2025)
by: Jendal, Theis E., et al.
Published: (2025)
Training-free Graph-based Imputation of Missing Modalities in Multimodal Recommendation
by: Malitesta, Daniele, et al.
Published: (2026)
by: Malitesta, Daniele, et al.
Published: (2026)
IA-GCN: Interactive Graph Convolutional Network for Recommendation
by: Zhang, Yinan, et al.
Published: (2022)
by: Zhang, Yinan, et al.
Published: (2022)
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?
by: Li, Ming, et al.
Published: (2024)
by: Li, Ming, et al.
Published: (2024)
HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph Recommendation
by: Zhang, Lu, et al.
Published: (2023)
by: Zhang, Lu, et al.
Published: (2023)
Light distillation for Incremental Graph Convolution Collaborative Filtering
by: Fan, X, et al.
Published: (2025)
by: Fan, X, et al.
Published: (2025)
Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation
by: Cheng, Zhiyong, et al.
Published: (2024)
by: Cheng, Zhiyong, et al.
Published: (2024)
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
by: Huang, Junjie, et al.
Published: (2024)
by: Huang, Junjie, et al.
Published: (2024)
Non-parametric Graph Convolution for Re-ranking in Recommendation Systems
by: Ouyang, Zhongyu, et al.
Published: (2025)
by: Ouyang, Zhongyu, et al.
Published: (2025)
TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency
by: Zou, Henry Peng, et al.
Published: (2025)
by: Zou, Henry Peng, et al.
Published: (2025)
SWGCN: Synergy Weighted Graph Convolutional Network for Multi-Behavior Recommendation
by: Chen, Fangda, et al.
Published: (2026)
by: Chen, Fangda, et al.
Published: (2026)
The Best is Yet to Come: Graph Convolution in the Testing Phase for Multimodal Recommendation
by: Xu, Jinfeng, et al.
Published: (2025)
by: Xu, Jinfeng, et al.
Published: (2025)
Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation
by: Wu, Guowei, et al.
Published: (2024)
by: Wu, Guowei, et al.
Published: (2024)
Rolling Forward: Enhancing LightGCN with Causal Graph Convolution for Credit Bond Recommendation
by: Ghiye, Ashraf, et al.
Published: (2025)
by: Ghiye, Ashraf, et al.
Published: (2025)
Amplify Graph Learning for Recommendation via Sparsity Completion
by: Yuan, Peng, et al.
Published: (2024)
by: Yuan, Peng, et al.
Published: (2024)
Adaptive Candidate Retrieval with Dynamic Knowledge Graph Construction for Cold-Start Recommendation
by: Yang, Wooseong, et al.
Published: (2025)
by: Yang, Wooseong, et al.
Published: (2025)
Post-Training Denoising of User Profiles with LLMs in Collaborative Filtering Recommendation
by: Dervishaj, Ervin, et al.
Published: (2026)
by: Dervishaj, Ervin, et al.
Published: (2026)
Multi-Graph Co-Training for Capturing User Intent in Session-based Recommendation
by: Yang, Zhe, et al.
Published: (2024)
by: Yang, Zhe, et al.
Published: (2024)
Distance-aware Self-adaptive Graph Convolution for Fine-grained Hierarchical Recommendation
by: Huang, Tao, et al.
Published: (2025)
by: Huang, Tao, et al.
Published: (2025)
COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal Recommendation
by: Xu, Jinfeng, et al.
Published: (2025)
by: Xu, Jinfeng, et al.
Published: (2025)
Knowledge Graph Tokenization for Behavior-Aware Generative Next POI Recommendation
by: Sun, Ke, et al.
Published: (2025)
by: Sun, Ke, et al.
Published: (2025)
Similar Items
-
Mixed Supervised Graph Contrastive Learning for Recommendation
by: Zhang, Weizhi, et al.
Published: (2024) -
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
by: Zhang, Weizhi, et al.
Published: (2025) -
Graph Neural Controlled Differential Equations For Collaborative Filtering
by: Xu, Ke, et al.
Published: (2025) -
Training Large Recommendation Models via Graph-Language Token Alignment
by: Yang, Mingdai, et al.
Published: (2025) -
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?
by: Malitesta, Daniele, et al.
Published: (2024)