In recent years, Recommender Systems(RS) have witnessed a transformative shift with the advent of Large Language Models(LLMs) in the field of Natural Language Processing(NLP). These models such as OpenAI's GPT-3.5/4, Llama from Meta, have demonstrated unprecedented capabilities in understanding and generating human-like text. This has led to a paradigm shift in the realm of personalized and explainable recommendations, as LLMs offer a versatile toolset for processing vast amounts of textual data to enhance user experiences. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey aims to analyze how RS can benefit from LLM-based methodologies. Furthermore, we describe major challenges in Personalized Explanation Generating(PEG) tasks, which are cold-start problems, unfairness and bias problems in RS.

翻译：近年来，随着自然语言处理（NLP）领域中大语言模型（LLMs）的兴起，推荐系统（RS）经历了变革性转变。这些模型，如OpenAI的GPT-3.5/4、Meta的Llama，在理解和生成类人文本方面展现出前所未有的能力。这导致了个性化与可解释推荐领域的范式转变，因为大语言模型提供了一套多功能工具集，用于处理海量文本数据以提升用户体验。为全面理解现有基于大语言模型的推荐系统，本综述旨在分析推荐系统如何从基于大语言模型的方法中受益。此外，我们阐述了在个性化解释生成（PEG）任务中的主要挑战，即推荐系统中的冷启动问题、不公平性与偏差问题。