DCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation
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,...
| Authors | Xinchi Zou, Tongzhenzhi Su, Jianjun Li et al. |
|---|---|
| Primary category | cs.IR |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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