E-commerce is growing at an unprecedented rate and the fashion industry has
recently witnessed a noticeable shift in customers' order behaviour towards
stronger online shopping. However, fashion articles ordered online do not
always find their way to a customer's wardrobe. In fact, a large share of them
end up being returned. Finding clothes that fit online is very challenging and
accounts for one of the main drivers of increased return rates in fashion
e-commerce. Size and fit related returns severely impact 1. the customers
experience and their dissatisfaction with online shopping, 2. the environment
through an increased carbon footprint, and 3. the profitability of online
fashion platforms. Due to poor fit, customers often end up returning articles
that they like but do not fit them, which they have to re-order in a different
size. To tackle this issue we introduce SizeFlags, a probabilistic Bayesian
model based on weakly annotated large-scale data from customers. Leveraging the
advantages of the Bayesian framework, we extend our model to successfully
integrate rich priors from human experts feedback and computer vision
intelligence. Through extensive experimentation, large-scale A/B testing and
continuous evaluation of the model in production, we demonstrate the strong
impact of the proposed approach in robustly reducing size-related returns in
online fashion over 14 countries.

该研究介绍了一种基于贝叶斯模型的解决方案，利用顾客提供的大规模低强度标注数据以及人工专家反馈和计算机视觉智能，成功地减少了 14 个国家在线时尚市场上与尺寸有关的退货率。