DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level
<|reference_start|>DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level: 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.<|reference_end|>
@article{gao2024dre:,
    title={DRE: Generating Recommendation Explanations by Aligning Large Language
  Models at Data-level},
    author={Shen Gao, Yifan Wang, Jiabao Fang, Lisi Chen, Peng Han, Shuo Shang},
    journal={arXiv preprint arXiv:2404.06311},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06311},
    primaryClass={cs.IR}
}