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.
@article{arxiv.2404.06311,
title = {DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level},
author = {Shen Gao and Yifan Wang and Jiabao Fang and Lisi Chen and Peng Han and Shuo Shang},
journal= {arXiv preprint arXiv:2404.06311},
year = {2024}
}