@inproceedings{wang-etal-2024-enhancing-high,
    title = "Enhancing High-order Interaction Awareness in {LLM}-based Recommender Model",
    author = "Wang, Xinfeng  and
      Cui, Jin  and
      Fukumoto, Fumiyo  and
      Suzuki, Yoshimi",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.emnlp-main.653/",
    doi = "10.18653/v1/2024.emnlp-main.653",
    pages = "11696--11711",
    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."
}