用大模型+特征分析，让普通人看懂饮食推荐理由。
A Lay User Explainable Food Recommendation System Based on Hybrid Feature Importance Extraction and Large Language Models
- 结合SHAP与大模型，动态提取关键推荐因素。
- 相比现有方法，解释更全面、易懂且有说服力。
- 适合普通用户理解复杂推荐结果，提升信任感。
近年来，大语言模型（LLM）发展迅速，应用广泛。本文利用LLM构建后处理机制，为食物推荐系统的结果提供更详尽的解释。通过将LLM与基于SHAP的混合特征重要性提取相结合，生成对普通用户而言更动态、可信且更全面的解释，优于文献中的现有方法。该方法提升了用户对复杂推荐结果的理解程度，增强了系统的透明度与可信度。
原文摘要 · Abstract (English)
Large Language Models (LLM) have experienced strong development in recent years, with varied applications. This paper uses LLMs to develop a post-hoc process that provides more elaborated explanations of the results of food recommendation systems. By combining LLM with a hybrid extraction of key variables using SHAP, we obtain dynamic, convincing and more comprehensive explanations to lay user, compared to those in the literature. This approach enhances user trust and transparency by making complex recommendation outcomes easier to understand for a lay user.
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