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.

本研究针对现有基于大型语言模型的推荐系统忽略或低效建模用户-项目高阶交互的问题，提出了增强型推荐模型ELMRec。该模型通过增强整体词嵌入来显著提升对图构建交互的理解，并提出了一种重新排序解决方案，使得ELMRec在直接推荐和序列推荐中均超越了最先进的方法。

增强基于大型语言模型的推荐系统中的高阶交互意识