A Survey on Large Language Models for Personalized and Explainable Recommendations

TL;DR

This survey reviews how Large Language Models are revolutionizing personalized and explainable recommender systems, highlighting benefits, challenges, and future directions in leveraging LLMs for improved user experience.

Contribution

It provides a comprehensive overview of LLM-based recommendation systems, analyzing their advantages, challenges like cold-start and bias, and potential research directions.

Findings

LLMs significantly enhance personalized recommendations and explanations.

Challenges include cold-start issues, unfairness, and bias in LLM-based RS.

LLMs offer versatile tools for processing textual data in RS.

Abstract

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.