  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.
