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

• URL: http://arxiv.org/abs/2409.19979v2
• Date: Tue, 1 Oct 2024 13:04:55 GMT
• Title: Enhancing High-order Interaction Awareness in LLM-based Recommender Model
• Authors: Xinfeng Wang, Jin Cui, Fumiyo Fukumoto, Yoshimi Suzuki,
• Abstract summary: 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. Our ELMRec outperforms state-of-the-art (SOTA) methods in both direct and sequential recommendations.
• Score: 3.7623606729515133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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