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Interaction-Grounded Semantic Graph Refinement for LLM-Based Recommendation
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DOI：10.1109/ACCESS.2025.3631801.png)
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摘要
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Large language models (LLMs) have recently demonstrated remarkable potential for recommendation by reframing it as a text-generation task. Recent LLM-based approaches apply GNNs to capture higher-order collaborative patterns from interaction graphs but handle textual data separately, failing to extract higher-order semantic patterns from text. One potential solution is to construct semantic graphs to better capture such semantic relationships. However, naively selecting top-k connections by profile similarity introduces significant challenges: 1) preference gap between textual similarity and actual user behavior, and 2) semantic distortion where identical attributes carry different meanings for users versus items. To overcome these issues, we propose an Interaction-Grounded Semantic Recommender (IGSRec), which constructs an interaction-grounded semantic graph by aligning profile-based connections with observed interactions. IGSRec employs an LLM-based profile generator, constructs a top-k semantic graph, then refines it using a learnable scoring function that identifies relevant semantic neighborhoods conditioned on user-item interactions. Through dual-graph propagation over both the refined semantic and interaction graphs, IGSRec captures higher-order semantic and collaborative patterns. Experiments on Amazon Review benchmarks demonstrate state-of-the-art performance in both direct and sequential recommendation tasks. Our code and data are publicly available at https://github.com/oseoko/IGSRec/
Keyword:
Semantics
Distortion
Collaboration
Cognition
Reviews
Knowledge graphs
Vectors
Large language models
History
Faces
recommendation systems
graph refinement
graph neural networks
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
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