用成分提取美妆属性，让推荐更可信可解释。
Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes
- 基于成分的端到端学习，用能量模型提取美妆属性
- 准确率高、可解释性强，支持后续属性扩展
- 适合需要透明推荐的电商平台和用户
精准提取属性对美妆产品推荐和建立客户信任至关重要。然而，现有方法往往不可靠且不完整。本文提出一种基于美妆产品成分的端到端监督学习系统，采用新颖的能量基隐式模型架构。该架构在准确性、可解释性、鲁棒性和灵活性方面均表现优异。此外，模型可轻松微调以纳入新增属性，适用于真实场景。我们在一个大型电商护肤产品目录数据集上验证了模型有效性，并展示了基于成分的属性提取如何提升推荐的可解释性。
原文摘要 · Abstract (English)
Accurate attribute extraction is critical for beauty product recommendations and building trust with customers. This remains an open problem, as existing solutions are often unreliable and incomplete. We present a system to extract beauty-specific attributes using end-to-end supervised learning based on beauty product ingredients. A key insight to our system is a novel energy-based implicit model architecture. We show that this implicit model architecture offers significant benefits in terms of accuracy, explainability, robustness, and flexibility. Furthermore, our implicit model can be easily fine-tuned to incorporate additional attributes as they become available, making it more useful in real-world applications. We validate our model on a major e-commerce skincare product catalog dataset and demonstrate its effectiveness. Finally, we showcase how ingredient-based attribute extraction contributes to enhancing the explainability of beauty recommendations.
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