  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.
