Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes
<|reference_start|>Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes: 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.<|reference_end|>
@article{liu2024beauty,
    title={Beauty Beyond Words: Explainable Beauty Product Recommendations Using
  Ingredient-Based Product Attributes},
    author={Siliang Liu, Rahul Suresh, Amin Banitalebi-Dehkordi},
    journal={arXiv preprint arXiv:2409.13628},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13628},
    primaryClass={cs.LG cs.IR}
}