In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommendation systems remains relatively unexplored. This paper presents an innovative approach to recommendation systems using large language models (LLMs) based on text data. In this paper, we present a novel text-based large language model for recommendation (TBLLMR) that utilized the expressive power of LLM to generate personalized recommendation. TBLLMR uses LLM's understanding ability to interpret context, learn user preferences, and generate relevant recommendation. Our proposed approach leverages the vast knowledge encoded in large language models to accomplish recommendation tasks. We first we formulate specialized prompts to enhance the ability of LLM to comprehend recommendation tasks. Subsequently, we use these prompts to fine-tune the model on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics. Our research underscores the potential of text-based LLMs in revolutionizing the domain of recommendation systems and offers a foundational framework for future explorations in this field. We conduct extensive experiments on benchmark datasets, and the experiments shows that our TBLLMR has significant better results on large dataset.

翻译：近年来，大型语言模型（LLM）已成为处理多种自然语言任务的有力工具。然而，它们在推荐系统中的潜力仍相对未被充分探索。本文提出了一种创新的推荐系统方法，采用基于文本数据的大型语言模型（LLM）。我们提出了一种新颖的基于文本的大型语言模型用于推荐（TBLLMR），该模型利用LLM的表达能力生成个性化推荐。TBLLMR借助LLM的理解能力来解读上下文、学习用户偏好并生成相关推荐。我们提出的方法利用大型语言模型中编码的丰富知识来完成推荐任务。首先，我们设计专门的提示词以增强LLM理解推荐任务的能力。随后，我们使用这些提示词在用户-项目交互数据集（以文本数据表示）上对模型进行微调，以捕捉用户偏好和项目特征。我们的研究强调了基于文本的LLM在革新推荐系统领域的潜力，并为该领域的未来探索提供了基础框架。我们在基准数据集上进行了大量实验，结果表明，我们的TBLLMR在大型数据集上取得了显著更好的结果。