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arXiv · 2404.06311

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

Shen Gao·Yifan Wang·Jiabao Fang·Lisi Chen·Peng Han·Shuo Shang
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Abstract

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.

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Shen Gao, Yifan Wang, Jiabao Fang, Lisi Chen, Peng Han, Shuo Shang. 2024-04-09. DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level. https://arxiv.org/abs/2404.06311

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