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

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 aspect