A Survey on Large Language Models for Personalized and Explainable Recommendations
This is an incremental survey paper that synthesizes existing research on applying LLMs to recommender systems for researchers and practitioners.
This survey analyzes how large language models (LLMs) can enhance recommender systems for personalized and explainable recommendations, identifying key challenges including cold-start problems and bias issues.
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.