# SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce

arXiv: https://arxiv.org/abs/2106.03532 · HTML: https://mlsanity.com/paper/2106.03532/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [VITON: An Image-based Virtual Try-on Network](https://mlsanity.com/paper/1711.08447/) (2017)
- [Towards Multi-pose Guided Virtual Try-on Network](https://mlsanity.com/paper/1902.11026/) (2019)
- [ViBE: Dressing for Diverse Body Shapes](https://mlsanity.com/paper/1912.06697/) (2019)
- [A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce](https://mlsanity.com/paper/1907.09844/) (2019)
- [A Hierarchical Bayesian Model for Size Recommendation in Fashion](https://mlsanity.com/paper/1908.00825/) (2019)
- [SizeNet: Weakly Supervised Learning of Visual Size and Fit in Fashion Images](https://mlsanity.com/paper/1905.11784/) (2019)
- [Automated Fashion Size Normalization](https://mlsanity.com/paper/1908.09980/) (2019)
- [Early Bird Catches the Worm: Predicting Returns Even Before Purchase in Fashion E-commerce](https://mlsanity.com/paper/1906.12128/) (2019)

## Built on by (most cited first, 1 of 1)

- [Tailor: Size Recommendations for High-End Fashion Marketplaces](https://mlsanity.com/paper/2401.01978/) (2024)

## Abstract (arXiv)

E-commerce is growing at an unprecedented rate and the fashion industry has recently witnessed a noticeable shift in customers' order behaviour towards stronger online shopping. However, fashion articles ordered online do not always find their way to a customer's wardrobe. In fact, a large share of them end up being returned. Finding clothes that fit online is very challenging and accounts for one of the main drivers of increased return rates in fashion e-commerce. Size and fit related returns severely impact 1. the customers experience and their dissatisfaction with online shopping, 2. the environment through an increased carbon footprint, and 3. the profitability of online fashion platforms. Due to poor fit, customers often end up returning articles that they like but do not fit them, which they have to re-order in a different size. To tackle this issue we introduce SizeFlags, a probabilistic Bayesian model based on weakly annotated large-scale data from customers. Leveraging the advantages of the Bayesian framework, we extend our model to successfully integrate rich priors from human experts feedback and computer vision intelligence. Through extensive experimentation, large-scale A/B testing and continuous evaluation of the model in production, we demonstrate the strong impact of the proposed approach in robustly reducing size-related returns in online fashion over 14 countries.