Product Review Image Ranking for Fashion E-commerce

TL;DR

This paper introduces a new training method for ranking customer images in fashion e-commerce, improving the relevance of displayed images to enhance online shopping decisions.

Contribution

It presents a simple training procedure and a dataset for ranking customer images, effectively distinguishing high-quality images from low-quality ones.

Findings

Outperforms baseline models on correlation coefficient metric.

Achieves higher accuracy in image ranking.

Effective in prioritizing relevant customer images.

Abstract

In a fashion e-commerce platform where customers can't physically examine the products on their own, being able to see other customers' text and image reviews of the product is critical while making purchase decisions. Given the high reliance on these reviews, over the years we have observed customers proactively sharing their reviews. With an increase in the coverage of User Generated Content (UGC), there has been a corresponding increase in the number of customer images. It is thus imperative to display the most relevant images on top as it may influence users' online shopping choices and behavior. In this paper, we propose a simple yet effective training procedure for ranking customer images. We created a dataset consisting of Myntra (A Major Indian Fashion e-commerce company) studio posts and highly engaged (upvotes/downvotes) UGC images as our starting point and used selected distortion techniques on the images of the above dataset to bring their quality at par with those of bad UGC images. We train our network to rank bad-quality images lower than high-quality ones. Our proposed method outperforms the baseline models on two metrics, namely correlation coefficient, and accuracy, by substantial margins.