# A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce

arXiv: https://arxiv.org/abs/1907.09844 · HTML: https://mlsanity.com/paper/1907.09844/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 10 of 10)

- [PreSizE: Predicting Size in E-Commerce using Transformers](https://mlsanity.com/paper/2105.01564/) (2021)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [FitGAN: Fit- and Shape-Realistic Generative Adversarial Networks for Fashion](https://mlsanity.com/paper/2206.11768/) (2022)
- [SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce](https://mlsanity.com/paper/2106.03532/) (2021)
- [Incorporating Customer Reviews in Size and Fit Recommendation systems for Fashion E-Commerce](https://mlsanity.com/paper/2208.06261/) (2022)
- [Human Body Shape Classification Based on a Single Image](https://mlsanity.com/paper/2305.18480/) (2023)
- [Tailor: Size Recommendations for High-End Fashion Marketplaces](https://mlsanity.com/paper/2401.01978/) (2024)
- [Agentic Personalized Fashion Recommendation in the Age of Generative AI: Challenges, Opportunities, and Evaluation](https://mlsanity.com/paper/2508.02342/) (2025)
- [Cyberswarm: a novel swarm intelligence algorithm inspired by cyber community dynamics](https://mlsanity.com/paper/2512.14752/) (2025)
- [DeepAffinity: Long-Term Aspect Preference Prediction in eCommerce using Small Language Models](https://mlsanity.com/paper/2609.02468/) (2026)

## Abstract (arXiv)

Personalized size and fit recommendations bear crucial significance for any fashion e-commerce platform. Predicting the correct fit drives customer satisfaction and benefits the business by reducing costs incurred due to size-related returns. Traditional collaborative filtering algorithms seek to model customer preferences based on their previous orders. A typical challenge for such methods stems from extreme sparsity of customer-article orders. To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation. Our proposed method can ingest arbitrary customer and article data and can model multiple individuals or intents behind a single account. The method optimizes a global set of parameters to learn population-level abstractions of size and fit relevant information from observed customer-article interactions. It further employs customer and article specific embedding variables to learn their properties. Together with learned entity embeddings, the method maps additional customer and article attributes into a latent space to derive personalized recommendations. Application of our method to two publicly available datasets demonstrate an improvement over the state-of-the-art published results. On two proprietary datasets, one containing fit feedback from fashion experts and the other involving customer purchases, we further outperform comparable methodologies, including a recent Bayesian approach for size recommendation.