Summary of Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-based Product Attributes, by Siliang Liu et al.
Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes
by Siliang Liu, Rahul Suresh, Amin Banitalebi-Dehkordi
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a system for extracting accurate beauty-specific attributes from products using end-to-end supervised learning based on ingredients. The system utilizes a novel energy-based implicit model architecture that improves accuracy, explainability, robustness, and flexibility compared to existing solutions. The model can be fine-tuned to incorporate additional attributes, making it more useful in real-world applications. The authors validate the model on a major e-commerce skincare product catalog dataset and demonstrate its effectiveness in enhancing the explainability of beauty recommendations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves an open problem in beauty product recommendation by creating a system that accurately extracts attributes from products. This helps build trust with customers. The system uses special computer learning to identify important ingredients in beauty products. It’s better than other methods because it’s accurate, easy to understand, and can be used on different types of products. The authors tested the system on a big database of skincare products and showed that it works well. |
Keywords
» Artificial intelligence » Supervised
