Product Review Image Ranking for Fashion E-commerce
Sangeet Jaiswal, Dhruv Patel, Sreekanth Vempati, Konduru Saiswaroop

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Retrieval and Classification Techniques · Sentiment Analysis and Opinion Mining · Generative Adversarial Networks and Image Synthesis
