Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes

TL;DR

This paper introduces an end-to-end supervised learning system using a novel energy-based implicit model to extract beauty-specific attributes from ingredients, improving accuracy, explainability, and adaptability for product recommendations.

Contribution

The paper presents a new energy-based implicit model architecture for ingredient-based attribute extraction, enhancing explainability and robustness in beauty product recommendations.

Findings

Improved attribute extraction accuracy on skincare datasets

Enhanced explainability of beauty recommendations

Model easily fine-tuned for new attributes

Abstract

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.