mlsanityData to 14 Oct 2026

DCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation

arXiv 2605.07314 ↗First posted 8 May 2026cs.IR cs.AIcited by 0

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Abstract (arXiv)

Knowledge Graphs (KGs) have proven highly effective for recommendation systems by capturing latent item relationships, while recent integration of Large Language Models (LLMs) has further enhanced semantic understanding and addressed knowledge sparsity issues. Nevertheless, current KG-and-LLM-based methods still face three main limitations: 1) inadequate modeling of implicit semantic relationships beyond explicit KG links; 2) suboptimal single-channel fusion of ID and LLM embeddings, which often leads to signal interference and blurred representations; and 3) insufficient consideration of user-item interaction frequency variations in recommendation strategies. To address these challenges, we propose the Dual-Channel Graph Learning (DCGL) framework, featuring three key innovations: 1) a dual-channel architecture that structurally decouples rich semantic information from user behavioral patterns, preventing early interference; 2) a multi-level contrastive learning mechanism that enhances robustness against KG noise through intra-view contrasts and bridges semantic gaps between channels via inter-view alignment; and 3) a dynamic fusion mechanism that adaptively balances semantic generalization and behavioral specificity based on interaction frequency, resolving the cascading limitation. Extensive experiments on four real-world datasets show that DCGL consistently outperforms state-of-the-art methods, yielding substantial improvements in sparse scenarios while maintaining precision for active users. Our code is available at https://github.com/XinchiZou/DCGL.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 40 references that are in the map. First posted on arXiv 8 May 2026.

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References · 31

  1. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  2. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 520
  3. 2023A Survey on Large Language Models for Recommendationcited by 379
  4. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 343
  5. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 337
  6. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 309
  7. 2020Self-supervised Graph Learning for Recommendationcited by 305
  8. 2017Neural Factorization Machines for Sparse Predictive Analyticscited by 183
  9. 2021Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendationcited by 173
  10. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendationcited by 172
  11. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  12. 2021A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directionscited by 144
19 more
  1. 2018DKN: Deep Knowledge-Aware Network for News Recommendationcited by 137
  2. 2021Learning Intents behind Interactions with Knowledge Graph for Recommendationcited by 94
  3. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 90
  4. 2019Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferencescited by 87
  5. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 73
  6. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 70
  7. 2018Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendationcited by 39
  8. 2022Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender Systemcited by 30
  9. 2025G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendationcited by 27
  10. 2023Knowledge Graph Self-Supervised Rationalization for Recommendationcited by 22
  11. 2018Explainable Reasoning over Knowledge Graphs for Recommendationcited by 19
  12. 2024SPRec: Self-Play to Debias LLM-based Recommendationcited by 18
  13. 2024Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graphcited by 16
  14. 2020Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graphcited by 12
  15. 2024Comprehending Knowledge Graphs with Large Language Models for Recommender Systemscited by 9
  16. 2024Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Modelscited by 6
  17. 2024Knowledge Enhanced Multi-intent Transformer Network for Recommendationcited by 2
  18. 2024RecLM: Recommendation Instruction Tuningcited by 2
  19. 2025LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecturecited by 1

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