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.

翻译：准确的属性提取对于美妆产品推荐和建立客户信任至关重要。这仍然是一个悬而未决的问题，因为现有解决方案往往不可靠且不完整。我们提出一个系统，基于美妆产品成分，利用端到端监督学习来提取美妆专用属性。我们系统的一个关键见解是一种新颖的基于能量的隐式模型架构。我们证明，这种隐式模型架构在准确性、可解释性、鲁棒性和灵活性方面具有显著优势。此外，我们的隐式模型可以轻松微调以纳入新出现的额外属性，从而使其在现实应用中更具实用性。我们在一个大型电子商务护肤产品目录数据集上验证了我们的模型，并证明了其有效性。最后，我们展示了基于成分的属性提取如何有助于增强美妆推荐的可解释性。