AI in Fashion Size & Fit
Online fashion shopping has been increasingly attracting customers at an unprecedented rate, yet choosing the right size and fit remains a major challenge. The absence of the sensory feedback leads to uncertainties in the buying decision and the hurdle of returning items that don't fit well. This causes frustration on the customer side and a large ecological and economical footprint on the business side. Recent research work on determining the right size and fit for customers is still in its infancy and remains very challenging. In this talk, we will navigate through the complex size & fit problem space and focus on how intelligent size and fit recommendation systems and machine learning solutions leverage different data sources to address this challenging problem.
Key takeaways 1) Fashion size and fit is a complex multifaceted problem that entails items and customers challenges. 2) Tackling this problem has a huge potential on both customers' experience and the environment. 3) How to employ different data sources and machine learning solutions to provide customers with size information and advice in various forms.
Nour is a senior applied scientist at Zalando focused on computer vision and machine learning-driven solutions to tackle the complex challenge of delivering size and fit advice to millions of customers. In parallel, she's on the organizing committee of Zalando Data Science Community (DSC) and Zalando Women in Tech Employee Resource Group (ZWT ERG). Outside of Zalando, Nour is co-organizing Women in Computer Vision (WiCV) workshop at the CVPR conference aiming to raise the visibility of female researchers and sharing experiences between students and professionals. She completed a Master degree in Computer Science from Saarland University - Max Planck Institute for Informatics. In her free time, she enjoys walking in nature, video games, and cooking.