In recent years, large language models (LLM) have emerged as powerful tools for diverse natural language processing tasks. However, their potential for recommender systems under the generative recommendation paradigm 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 LLM for generative recommendation (GenRec) that utilized the expressive power of LLM to directly generate the target item to recommend, rather than calculating ranking score for each candidate item one by one as in traditional discriminative recommendation. GenRec 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 LLaMA backbone LLM on a dataset of user-item interactions, represented by textual data, to capture user preferences and item characteristics. Our research underscores the potential of LLM-based generative recommendation 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 GenRec has significant better results on large dataset.

翻译：近年来，大语言模型（LLM）已成为处理多种自然语言任务的有力工具。然而，在生成式推荐范式下，其对推荐系统的潜力尚未得到充分探索。本文提出了一种基于文本数据、利用大语言模型（LLM）的创新推荐系统方法。我们介绍了一种新型大语言模型用于生成式推荐（GenRec），该模型借助LLM的表达能力直接生成推荐目标项，而非像传统判别式推荐那样逐一计算每个候选物品的排序分数。GenRec利用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。我们提出的方法充分利用了大语言模型中编码的丰富知识来完成推荐任务。首先，我们设计了专门的提示词以增强LLM理解推荐任务的能力。随后，基于用户-物品交互的文本表示数据集，我们使用这些提示词对LLaMA基础LLM进行微调，以捕捉用户偏好与物品特征。本研究凸显了基于LLM的生成式推荐在革新推荐系统领域方面的潜力，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广泛实验，结果表明GenRec在大型数据集上取得了显著更优的性能。