# DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level

arXiv: https://arxiv.org/abs/2404.06311 · HTML: https://mlsanity.com/paper/2404.06311/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2024.

## Built on by (most cited first, 4 of 4)

- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [CARTS: Collaborative Agents for Recommendation Textual Summarization](https://mlsanity.com/paper/2506.17765/) (2025)
- ["This Suits You the Best": Query Focused Comparative Explainable Summarization](https://mlsanity.com/paper/2507.04733/) (2025)
- [MetaSynth: Multi-Agent Metadata Generation from Implicit Feedback in Black-Box Systems](https://mlsanity.com/paper/2510.01523/) (2025)

## Abstract (arXiv)

Recommendation systems play a crucial role in various domains, suggesting items based on user behavior.However, the lack of transparency in presenting recommendations can lead to user confusion. In this paper, we introduce Data-level Recommendation Explanation (DRE), a non-intrusive explanation framework for black-box recommendation models.Different from existing methods, DRE does not require any intermediary representations of the recommendation model or latent alignment training, mitigating potential performance issues.We propose a data-level alignment method, leveraging large language models to reason relationships between user data and recommended items.Additionally, we address the challenge of enriching the details of the explanation by introducing target-aware user preference distillation, utilizing item reviews. Experimental results on benchmark datasets demonstrate the effectiveness of the DRE in providing accurate and user-centric explanations, enhancing user engagement with recommended item.