# A Hierarchical Bayesian Model for Size Recommendation in Fashion

arXiv: https://arxiv.org/abs/1908.00825 · HTML: https://mlsanity.com/paper/1908.00825/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 6 of 6)

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

## Abstract (arXiv)

We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion. Our approach jointly models a size purchased by a customer, and its possible return event: 1. no return, 2. returned too small 3. returned too big. Those events are drawn following a multinomial distribution parameterized on the joint probability of each event, built following a hierarchy combining priors. Such a model allows us to incorporate extended domain expertise and article characteristics as prior knowledge, which in turn makes it possible for the underlying parameters to emerge thanks to sufficient data. Experiments are presented on real (anonymized) data from millions of customers along with a detailed discussion on the efficiency of such an approach within a large scale production system.