# Incorporating Customer Reviews in Size and Fit Recommendation systems for Fashion E-Commerce

arXiv: https://arxiv.org/abs/2208.06261 · HTML: https://mlsanity.com/paper/2208.06261/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 11 Aug 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [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)
- [PreSizE: Predicting Size in E-Commerce using Transformers](https://mlsanity.com/paper/2105.01564/) (2021)
- [Analyzing Customer Feedback for Product Fit Prediction](https://mlsanity.com/paper/1908.10896/) (2019)

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

- [RulerNet: Learning Perspective-Invariant Ruler Representations for Robust Image Scale Estimation](https://mlsanity.com/paper/2507.07077/) (2025)

## Abstract (arXiv)

With the huge growth in e-commerce domain, product recommendations have become an increasing field of interest amongst e-commerce companies. One of the more difficult tasks in product recommendations is size and fit predictions. There are a lot of size related returns and refunds in e-fashion domain which causes inconvenience to the customers as well as costs the company. Thus having a good size and fit recommendation system, which can predict the correct sizes for the customers will not only reduce size related returns and refunds but also improve customer experience. Early works in this field used traditional machine learning approaches to estimate customer and product sizes from purchase history. These methods suffered from cold start problem due to huge sparsity in the customer-product data. More recently, people have used deep learning to address this problem by embedding customer and product features. But none of them incorporates valuable customer feedback present on product pages along with the customer and product features. We propose a novel approach which can use information from customer reviews along with customer and product features for size and fit predictions. We demonstrate the effectiveness of our approach compared to using just product and customer features on 4 datasets. Our method shows an improvement of 1.37% - 4.31% in F1 (macro) score over the baseline across the 4 different datasets.