1. Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes
- Author
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Liu, Siliang, Suresh, Rahul, and Banitalebi-Dehkordi, Amin
- Subjects
Computer Science - Machine Learning ,Computer Science - Information Retrieval - 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., Comment: 18th ACM Conference on Recommender Systems, Workshop on Strategic and Utility-aware REcommendation
- Published
- 2024