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With the huge growth in e-commerce domain, product recommendations havebecome an increasing field of interest amongst e-commerce companies. One of themore difficult tasks in product recommendations is size and fit predictions.There are a lot of size related returns and refunds in e-fashion domain whichcauses inconvenience to the customers as well as costs the company. Thus havinga good size and fit recommendation system, which can predict the correct sizesfor the customers will not only reduce size related returns and refunds butalso improve customer experience. Early works in this field used traditionalmachine learning approaches to estimate customer and product sizes frompurchase history. These methods suffered from cold start problem due to hugesparsity in the customer-product data. More recently, people have used deeplearning to address this problem by embedding customer and product features.But none of them incorporates valuable customer feedback present on productpages along with the customer and product features. We propose a novel approachwhich can use information from customer reviews along with customer and productfeatures for size and fit predictions. We demonstrate the effectiveness of ourapproach compared to using just product and customer features on 4 datasets.Our method shows an improvement of 1.37% - 4.31% in F1 (macro) score over thebaseline across the 4 different datasets.

Incorporating Customer Reviews in Size and Fit Recommendation systems for Fashion E-Commerce