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

• URL: http://arxiv.org/abs/2409.13628v1
• Date: Fri, 20 Sep 2024 16:36:46 GMT
• Title: Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes
• Authors: Siliang Liu, Rahul Suresh, Amin Banitalebi-Dehkordi,
• Abstract summary: 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.
• Score: 7.9177626516199435
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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