On Explaining Recommendations with Large Language Models: A Review

TL;DR

This review examines the emerging use of Large Language Models in generating explanations for recommender systems, highlighting early-stage research, challenges, and future directions for enhancing transparency and user trust.

Contribution

It provides a systematic review of current methodologies and identifies gaps in applying LLMs for explainable recommendations, guiding future research.

Findings

LLMs have potential to improve recommendation explanations

Current research on LLM-based explanations is scarce and early-stage

Identified challenges include data quality and explanation consistency

Abstract

The rise of Large Language Models (LLMs), such as LLaMA and ChatGPT, has opened new opportunities for enhancing recommender systems through improved explainability. This paper provides a systematic literature review focused on leveraging LLMs to generate explanations for recommendations -- a critical aspect for fostering transparency and user trust. We conducted a comprehensive search within the ACM Guide to Computing Literature, covering publications from the launch of ChatGPT (November 2022) to the present (November 2024). Our search yielded 232 articles, but after applying inclusion criteria, only six were identified as directly addressing the use of LLMs in explaining recommendations. This scarcity highlights that, despite the rise of LLMs, their application in explainable recommender systems is still in an early stage. We analyze these select studies to understand current methodologies, identify challenges, and suggest directions for future research. Our findings underscore the potential of LLMs improving explanations of recommender systems and encourage the development of more transparent and user-centric recommendation explanation solutions.