Year: 2026
Type: conference-paper
Abstract: Large Language Models (LLMs) have shown strong potential for recommendation (LLM-Rec) due to their powerful reasoning and generalization abilities.However, effectively aligning the textual semantics modeled by LLMs with the collaborative signals remains a key challenge.Existing methods either translate collaborative information into textual prompts or inject pre-trained embeddings into the LLM, both of which treat structural information as static input and fail to capture high-order relational dependencies.To bridge this gap, we propose GraphLoRA, a novel framework that generalizes low-rank adaptation from independent to structure-aware propagation.GraphLoRA embeds a trainable graph message-passing network within the low-rank adaptation pathway, enabling structural signals to propagate through the parameter space.This design allows collaborative topology to explicitly guide parameter updates, fostering deep integration between graph-structured and textual semantic information.Extensive experiments on multiple benchmarks demonstrate that GraphLoRA not only outperforms state-of-the-art LLM-based recommendation methods but also achieves superior generalization, effectively balancing structural reasoning capability with computational efficiency.Code is available at https://github.com/wgj15965/GraphLoRA.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166866014