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        <title>Enhancing High-order Interaction Awareness in LLM-based Recommender Model</title>
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        <namePart type="given">Xinfeng</namePart>
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    <name type="personal">
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        <namePart type="family">Fukumoto</namePart>
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        <namePart type="given">Yoshimi</namePart>
        <namePart type="family">Suzuki</namePart>
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            <namePart type="given">Yun-Nung</namePart>
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    <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, especially achieving a 124.3% to 293.7% improvement over SOTA LLM-based methods in direct recommendations. Our code is available online.</abstract>
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%0 Conference Proceedings
%T Enhancing High-order Interaction Awareness in LLM-based Recommender Model
%A Wang, Xinfeng
%A Cui, Jin
%A Fukumoto, Fumiyo
%A Suzuki, Yoshimi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wang-etal-2024-enhancing-high
%X 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, especially achieving a 124.3% to 293.7% improvement over SOTA LLM-based methods in direct recommendations. Our code is available online.
%U https://aclanthology.org/2024.emnlp-main.653
%P 11696-11711

Markdown (Informal)

[Enhancing High-order Interaction Awareness in LLM-based Recommender Model](https://aclanthology.org/2024.emnlp-main.653) (Wang et al., EMNLP 2024)

• Enhancing High-order Interaction Awareness in LLM-based Recommender Model (Wang et al., EMNLP 2024)

ACL

• Xinfeng Wang, Jin Cui, Fumiyo Fukumoto, and Yoshimi Suzuki. 2024. Enhancing High-order Interaction Awareness in LLM-based Recommender Model. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11696–11711, Miami, Florida, USA. Association for Computational Linguistics.