MultiFlags: A Probabilistic Framework for Article-Based Size Advice in Fashion E-Commerce

Matthias Späth, Andrea Nestler, Henry Böddeker, Leonidas Lefakis, Yevgeniy Puzikov, Rodrigo Weffer, Nour Karessli, Nadja Klein, Reza Shirvany

2025

Abstract

Accurately modeling the size behavior of fashion articles at scale is a critical task for fashion e-commerce. However, it has proven to be highly challenging due to inconsistent sizing systems across countries, inconsistent garment design processes, and brand-specific sizing specifications. Widespread methods in the field focus primarily on giving customers rudimentary size recommendations (e.g., we recommend you size S) based on the customers’ purchase behavior and/or their size and fit preferences. These approaches fail to take into account the size and fit behavior of the article, for example their design cut, shape, material, etc. (or at best treat it with simplistic ad hoc assumptions), and in turn, not effectively reducing the high volume of online article returns due to size and fit. In this work, we propose a theoretically-motivated probabilistic framework, MultiFlags, which can significantly reduce size-related returns in fashion e-commerce thanks to modeling multiple aspects of article’s size and fit behavior. We also highlight how this framework enables a principled approach to article-based size advice, while leveraging data from multiple modalities. The results validate the competitiveness of the proposed framework in the state-of-the-art in several size advice scenarios that are critical for fashion e-commerce. The framework is deployed in production in a large e-commerce site, serving millions of customers and driving significant results.

Paper Citation

in Harvard Style

Späth M., Nestler A., Böddeker H., Lefakis L., Puzikov Y., Weffer R., Karessli N., Klein N. and Shirvany R. (2025). MultiFlags: A Probabilistic Framework for Article-Based Size Advice in Fashion E-Commerce. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 313-322. DOI: 10.5220/0013709900004000

in Bibtex Style

@conference{kdir25,
author={Matthias Späth and Andrea Nestler and Henry Böddeker and Leonidas Lefakis and Yevgeniy Puzikov and Rodrigo Weffer and Nour Karessli and Nadja Klein and Reza Shirvany},
title={MultiFlags: A Probabilistic Framework for Article-Based Size Advice in Fashion E-Commerce},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={313-322},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013709900004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - MultiFlags: A Probabilistic Framework for Article-Based Size Advice in Fashion E-Commerce
SN -
AU - Späth M.
AU - Nestler A.
AU - Böddeker H.
AU - Lefakis L.
AU - Puzikov Y.
AU - Weffer R.
AU - Karessli N.
AU - Klein N.
AU - Shirvany R.
PY - 2025
SP - 313
EP - 322
DO - 10.5220/0013709900004000
PB - SciTePress