# On Explaining Recommendations with Large Language Models: A Review

arXiv: https://arxiv.org/abs/2411.19576 · HTML: https://mlsanity.com/paper/2411.19576/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2024.

## Built on by (most cited first, 15 of 15)

- [Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View](https://mlsanity.com/paper/2505.03336/) (2025)
- [The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations](https://mlsanity.com/paper/2505.05016/) (2025)
- [Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations](https://mlsanity.com/paper/2507.13705/) (2025)
- [PersonaAgent with GraphRAG: Community-Aware Knowledge Graphs for Personalized LLM](https://mlsanity.com/paper/2511.17467/) (2025)
- [MATRAG: Multi-Agent Transparent Retrieval-Augmented Generation for Explainable Recommendations](https://mlsanity.com/paper/2604.20848/) (2026)
- [Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective](https://mlsanity.com/paper/2505.03828/) (2025)
- [LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking](https://mlsanity.com/paper/2506.07449/) (2025)
- [Evaluating Generative AI Tools for Personalized Offline Recommendations: A Comparative Study](https://mlsanity.com/paper/2508.03710/) (2025)
- [HELM: A Human-Centered Evaluation Framework for LLM-Powered Recommender Systems](https://mlsanity.com/paper/2601.19197/) (2026)
- [Beyond Pipelines: A Fundamental Study on the Rise of Generative-Retrieval Architectures in Web Research](https://mlsanity.com/paper/2602.17450/) (2026)
- [How Personal Characteristics Shape User Exploration of Diverse Movie Recommendations with a LLM-Based Multi-Agent System](https://mlsanity.com/paper/2604.24405/) (2026)
- [Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders](https://mlsanity.com/paper/2607.10235/) (2026)
- [Evaluating the Impact of Explainable AI on Trust in AI-Assisted Code Review](https://mlsanity.com/paper/2607.24601/) (2026)
- [Nudging Sustainable Choices through LLM-Generated Recommendation Explanations](https://mlsanity.com/paper/2607.25726/) (2026)
- [The Utility of LLMs in Recommender Systems Explanation Evaluation](https://mlsanity.com/paper/2609.01627/) (2026)

## Abstract (arXiv)

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.