Enhancing High-order Interaction Awareness in LLM-based Recommender Model
<|reference_start|>Enhancing High-order Interaction Awareness in LLM-based Recommender Model: 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.<|reference_end|>
@article{wang2024enhancing,
    title={Enhancing High-order Interaction Awareness in LLM-based Recommender
  Model},
    author={Xinfeng Wang, Jin Cui, Fumiyo Fukumoto and Yoshimi Suzuki},
    journal={arXiv preprint arXiv:2409.19979},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19979},
    primaryClass={cs.IR cs.CL}
}