Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes
This work addresses the need for trustworthy and accurate beauty product recommendations for e-commerce customers, though it appears incremental as it builds on existing supervised learning approaches with a novel architecture.
The paper tackles the problem of unreliable and incomplete attribute extraction for beauty product recommendations by introducing an end-to-end supervised learning system based on product ingredients, achieving significant improvements in accuracy, explainability, robustness, and flexibility as validated on a major e-commerce skincare dataset.
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.