  While shopping for fashion products, customers usually prefer to try-out
products to examine fit, material, overall look and feel. Due to lack of try
out options during online shopping, it becomes pivotal to provide customers
with as much of this information as possible to enhance their shopping
experience. Also it becomes essential to provide same experience for new
customers. Our work here focuses on providing a production ready size
recommendation system for shoes and address the challenge of providing
recommendation for users with no previous purchases on the platform. In our
work, we present a probabilistic approach based on user co-purchase data
facilitated by generating a brand-brand relationship graph. Specifically we
address two challenges that are commonly faced while implementing such
solution. 1. Sparse signals for less popular or new products in the system 2.
Extending the solution for new users. Further we compare and contrast this
approach with our previous work and show significant improvement both in
recommendation precision and coverage.
