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.

最近几年，推荐系统 (Recommender Systems) 在自然语言处理 (Natural Language Processing) 领域迎来了巨大的变革，得益于大型语言模型 (Large Language Models) 的出现。这些模型，如 OpenAI 的 GPT-3.5/4 和 Meta 的 Llama，在理解和生成类似人类的文本方面展示了前所未有的能力，从而在个性化和可解释性推荐领域引发了范式转变，因为大型语言模型提供了处理大量文本数据以增强用户体验的多功能工具集。为了全面了解现有的基于大型语言模型的推荐系统，本综述旨在分析推荐系统如何从大型语言模型的方法中受益。此外，我们还描述了个性化解释生成 (Personalized Explanation Generating) 任务中的主要挑战，包括冷启动问题、公平性和偏见问题。