A deep learning system for predicting size and fit in fashion e-commerce

Abstract

Personalized size and ft recommendations bear crucial signifcance for any fashion e-commerce platform. Predicting the correct ft drives customer satisfaction and benefts the business by reducing costs incurred due to size-related returns. Traditional collaborative fltering 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 ft 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 ft relevant information from observed customer-article interactions. It further employs customer and article specifc 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 ft feedback from fashion experts and the other involving customer purchases, we further outperform comparable methodologies, including a recent Bayesian approach for size recommendation.