Size and Fit Recommendations for Cold Start Customers in Fashion E-Commerce


Jaidam Ram Tej, Jyotirmoy Banerjee and Narendra Varma Dasararaju, Flipkart Internet Private Limited, India


Fashion e-commerce is expected to grow rapidly over the next few years. One of the main hurdles in fashion e-commerce is to recommend the right size to customers which helps customers in having a better online shopping experience. Hence Size and fit recommendation is an important problem which helps improve the confidence of a customer for making a purchase on an e-commerce platform. This also reduces the returns in fashion e-commerce. In this work we propose a novel bayesian probabilistic approach for non-personalised product size recommendation for customers. We use maximum likelihood estimation for estimating the parameters of our model. We use customer purchase and returns history to infer the true product size. Given a product we provide size recommendations to a customer, i.e. we suggest a customer to buy a size small, large or same size. In experiments with flipkart shoes datasets our model leads to an improvement of 3-4% AUC over the existing baseline. In Online AB testing for flipkart shoes categories our approach shows a performance improvement of returns by 12- 24 bps.


Fashion E-Commerce, Recommendation, Size And Fit, Maximum Likelihood, A/B Testing, Catalog