# Tailor: Size Recommendations for High-End Fashion Marketplaces

arXiv: https://arxiv.org/abs/2401.01978 · HTML: https://mlsanity.com/paper/2401.01978/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 3 Jan 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Footwear Size Recommendation System](https://mlsanity.com/paper/1806.11423/) (2018)
- [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)
- [PreSizE: Predicting Size in E-Commerce using Transformers](https://mlsanity.com/paper/2105.01564/) (2021)
- [SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce](https://mlsanity.com/paper/2106.03532/) (2021)
- [SizeGAN: Improving Size Representation in Clothing Catalogs](https://mlsanity.com/paper/2211.02892/) (2022)

## Abstract (arXiv)

In the ever-changing and dynamic realm of high-end fashion marketplaces, providing accurate and personalized size recommendations has become a critical aspect. Meeting customer expectations in this regard is not only crucial for ensuring their satisfaction but also plays a pivotal role in driving customer retention, which is a key metric for the success of any fashion retailer. We propose a novel sequence classification approach to address this problem, integrating implicit (Add2Bag) and explicit (ReturnReason) user signals. Our approach comprises two distinct models: one employs LSTMs to encode the user signals, while the other leverages an Attention mechanism. Our best model outperforms SFNet, improving accuracy by 45.7%. By using Add2Bag interactions we increase the user coverage by 24.5% when compared with only using Orders. Moreover, we evaluate the models' usability in real-time recommendation scenarios by conducting experiments to measure their latency performance.