# A Hierarchical Bayesian Model for Size Recommendation in Fashion

## TL;DR

This paper presents a hierarchical Bayesian model for size recommendation in fashion e-commerce, jointly modeling purchase and return events to improve personalization and leverage domain knowledge.

## Contribution

It introduces a novel hierarchical Bayesian framework that incorporates domain expertise and article features for size recommendation in large-scale fashion e-commerce.

## Key findings

- Effective modeling of purchase and return events
- Incorporation of domain knowledge improves predictions
- Scalability demonstrated on millions of customer data

## Abstract

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.