Enhancing High-order Interaction Awareness in LLM-based Recommender Model

Abstract:Large language models (LLMs) have demonstrated prominent reasoning capabilities in recommendation tasks by transforming them into text-generation tasks. However, existing approaches either disregard or ineffectively model the user-item high-order interactions. To this end, this paper presents an enhanced LLM-based recommender (ELMRec). We enhance whole-word embeddings to substantially enhance LLMs' interpretation of graph-constructed interactions for recommendations, without requiring graph pre-training. This finding may inspire endeavors to incorporate rich knowledge graphs into LLM-based recommenders via whole-word embedding. We also found that LLMs often recommend items based on users' earlier interactions rather than recent ones, and present a reranking solution. Our ELMRec outperforms state-of-the-art (SOTA) methods in both direct and sequential recommendations.

Long paper accepted to EMNLP 2024 Main. 16 pages

Information Retrieval (cs.IR); Computation and Language (cs.CL)

arXiv:2409.19979 [cs.IR]

(or arXiv:2409.19979v4 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2409.19979 Focus to learn more arXiv-issued DOI via DataCite

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