  Personalized size and fit recommendations bear crucial significance for any
fashion e-commerce platform. Predicting the correct fit drives customer
satisfaction and benefits the business by reducing costs incurred due to
size-related returns. Traditional collaborative filtering algorithms seek to
model customer preferences based on their previous orders. A typical challenge
for such methods stems from extreme sparsity of customer-article orders. To
alleviate this problem, we propose a deep learning based content-collaborative
methodology for personalized size and fit recommendation. Our proposed method
can ingest arbitrary customer and article data and can model multiple
individuals or intents behind a single account. The method optimizes a global
set of parameters to learn population-level abstractions of size and fit
relevant information from observed customer-article interactions. It further
employs customer and article specific embedding variables to learn their
properties. Together with learned entity embeddings, the method maps additional
customer and article attributes into a latent space to derive personalized
recommendations. Application of our method to two publicly available datasets
demonstrate an improvement over the state-of-the-art published results. On two
proprietary datasets, one containing fit feedback from fashion experts and the
other involving customer purchases, we further outperform comparable
methodologies, including a recent Bayesian approach for size recommendation.
