Recent advances in the e-commerce fashion industry have led to an exploration
of novel ways to enhance buyer experience via improved personalization.
Predicting a proper size for an item to recommend is an important
personalization challenge, and is being studied in this work. Earlier works in
this field either focused on modeling explicit buyer fitment feedback or
modeling of only a single aspect of the problem (e.g., specific category,
brand, etc.). More recent works proposed richer models, either content-based or
sequence-based, better accounting for content-based aspects of the problem or
better modeling the buyer's online journey. However, both these approaches fail
in certain scenarios: either when encountering unseen items (sequence-based
models) or when encountering new users (content-based models).
To address the aforementioned gaps, we propose PreSizE - a novel deep
learning framework which utilizes Transformers for accurate size prediction.
PreSizE models the effect of both content-based attributes, such as brand and
category, and the buyer's purchase history on her size preferences. Using an
extensive set of experiments on a large-scale e-commerce dataset, we
demonstrate that PreSizE is capable of achieving superior prediction
performance compared to previous state-of-the-art baselines. By encoding item
attributes, PreSizE better handles cold-start cases with unseen items, and
cases where buyers have little past purchase data. As a proof of concept, we
demonstrate that size predictions made by PreSizE can be effectively integrated
into an existing production recommender system yielding very effective features
and significantly improving recommendations.

本文提出了一个利用 Transformers 来预测商品适合买家尺寸的新型深度学习框架 PreSizE，通过集成商品属性和买家购买历史等内容来有效解决冷启动和买家数据不足等问题，与现有的基线模型相比，证明了 PreSizE 有着更优秀的预测表现，并且实现了与推荐系统的有效结合。

利用 Transformer 模型预测电商商品尺寸

PreSizE: Predicting Size in E-Commerce using Transformers

We introduce a hierarchical Bayesian approach to tackle the challenging
problem of size recommendation in e-commerce fashion. Our approach jointly
models a size purchased by a customer, and its possible return event: 1. no
return, 2. returned too small 3. returned too big. Those events are drawn
following a multinomial distribution parameterized on the joint probability of
each event, built following a hierarchy combining priors. Such a model allows
us to incorporate extended domain expertise and article characteristics as
prior knowledge, which in turn makes it possible for the underlying parameters
to emerge thanks to sufficient data. Experiments are presented on real
(anonymized) data from millions of customers along with a detailed discussion
on the efficiency of such an approach within a large scale production system.

该研究提出了一种层次贝叶斯方法来应对电商时尚中的尺码推荐问题，结合先验知识进行参数建模，发现该方法在大规模生产系统中十分高效。