# PreSizE: Predicting Size in E-Commerce using Transformers

arXiv: https://arxiv.org/abs/2105.01564 · HTML: https://mlsanity.com/paper/2105.01564/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1905.06874/) (2019)
- [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)

## Built on by (most cited first, 5 of 5)

- [Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce](https://mlsanity.com/paper/2110.11072/) (2021)
- [Incorporating Customer Reviews in Size and Fit Recommendation systems for Fashion E-Commerce](https://mlsanity.com/paper/2208.06261/) (2022)
- [RulerNet: Learning Perspective-Invariant Ruler Representations for Robust Image Scale Estimation](https://mlsanity.com/paper/2507.07077/) (2025)
- [Tailor: Size Recommendations for High-End Fashion Marketplaces](https://mlsanity.com/paper/2401.01978/) (2024)
- [DeepAffinity: Long-Term Aspect Preference Prediction in eCommerce using Small Language Models](https://mlsanity.com/paper/2609.02468/) (2026)

## Abstract (arXiv)

Recent advances in the e-commerce fashion industry have led to an exploration of novel ways to enhance buyer experience via improved personalization. Predicting a proper size for an item to recommend is an important personalization challenge, and is being studied in this work. Earlier works in this field either focused on modeling explicit buyer fitment feedback or modeling of only a single aspect of the problem (e.g., specific category, brand, etc.). More recent works proposed richer models, either content-based or sequence-based, better accounting for content-based aspects of the problem or better modeling the buyer's online journey. However, both these approaches fail in certain scenarios: either when encountering unseen items (sequence-based models) or when encountering new users (content-based models). To address the aforementioned gaps, we propose PreSizE - a novel deep learning framework which utilizes Transformers for accurate size prediction. PreSizE models the effect of both content-based attributes, such as brand and category, and the buyer's purchase history on her size preferences. Using an extensive set of experiments on a large-scale e-commerce dataset, we demonstrate that PreSizE is capable of achieving superior prediction performance compared to previous state-of-the-art baselines. By encoding item attributes, PreSizE better handles cold-start cases with unseen items, and cases where buyers have little past purchase data. As a proof of concept, we demonstrate that size predictions made by PreSizE can be effectively integrated into an existing production recommender system yielding very effective features and significantly improving recommendations.