# A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce

## TL;DR

This paper introduces a deep learning system that combines content and collaborative data to improve personalized size and fit recommendations in fashion e-commerce, reducing returns and increasing customer satisfaction.

## Contribution

It presents a novel deep learning approach that models multiple customer intents and incorporates diverse data sources for enhanced size and fit predictions.

## Key findings

- Outperforms state-of-the-art methods on public datasets
- Achieves better accuracy than recent Bayesian approaches
- Reduces size-related return rates in real-world applications

## Abstract

Personalized size and fit recommendations bear crucial significance for any fashion e-commerce platform. Predicting the correct fit drives customer satisfaction and benefits the business by reducing costs incurred due to size-related returns. Traditional collaborative filtering algorithms seek to model customer preferences based on their previous orders. A typical challenge for such methods stems from extreme sparsity of customer-article orders. To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation. Our proposed method can ingest arbitrary customer and article data and can model multiple individuals or intents behind a single account. The method optimizes a global set of parameters to learn population-level abstractions of size and fit relevant information from observed customer-article interactions. It further employs customer and article specific embedding variables to learn their properties. Together with learned entity embeddings, the method maps additional customer and article attributes into a latent space to derive personalized recommendations. Application of our method to two publicly available datasets demonstrate an improvement over the state-of-the-art published results. On two proprietary datasets, one containing fit feedback from fashion experts and the other involving customer purchases, we further outperform comparable methodologies, including a recent Bayesian approach for size recommendation.