LGMcRec: Large language models-augmented light graph model for multi-criteria recommendation

Zhenhua Meng, Fanshen Meng, Rongheng Lin, Budan Wu

Abstract: In the era of digital personalization, multi-criteria recommender systems (MCRSs) play a vital role in capturing the multi-dimensional nature of user preferences by considering multiple evaluation criteria rather than relying on a single overall rating. However, existing approaches to MCRSs face challenges in managing graph sparsity, criterion independence, and leveraging semantic information for recommendation tasks. To address these limitations, we propose a novel framework named Large Language Models-augmented Light Graph Model for Multi-criteria Recommendation (LGMcRec). LGMcRec integrates the strengths of graph neural networks (GNNs) and large language models (LLMs) to improve the representation and recommendation capabilities of MCRSs. In our model, we construct a tripartite graph structure that captures user-item interactions, item-criterion associations, and criterion interdependencies, effectively addressing issues of sparsity and unmodeled correlations in multi-criteria data. We extend the LightGCN architecture to learn embeddings over this graph, which are further enriched through semantic alignment with embeddings generated by LLMs from textual user and item profiles. To bridge the gap between graph-based and LLM-based embeddings, we employ a contrastive learning approach that maximizes the mutual information between the two embedding spaces, ensuring cohesive and comprehensive user and item representations. Experimental results on three MCRS datasets demonstrate that LGMcRec achieves significant performance improvements over state-of-the-art methods.

External IDs:dblp:journals/kbs/MengMLW25