超越言语之美：使用基于成分的产品属性的可解释美容产品推荐
Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes
AI总结：
针对美容产品属性提取不可靠且不完整的问题，提出一种基于成分的端到端监督学习系统，采用新颖的基于能量的隐式模型架构，在准确性、可解释性、鲁棒性和灵活性上表现优异，并能有效增强美容推荐的可解释性。
摘要：
准确的属性提取对于美容产品推荐和建立客户信任至关重要。由于现有解决方案通常不可靠且不完整，这仍然是一个开放问题。我们提出了一种基于美容产品成分，使用端到端监督学习来提取美容特定属性的系统。我们系统的关键见解是一种新颖的基于能量的隐式模型架构。我们表明，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提供了显著的好处。此外，我们的隐式模型可以轻松地进行微调，以在额外属性可用时将其纳入，使其在实际应用中更有用。我们在一个主要的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容推荐的可解释性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
