Search is at the heart of modern e-commerce. As a result, the task of ranking search results automatically (learning to rank) is a multibillion dollar machine learning problem. Traditional models optimize over a few hand-constructed features based on the item's text. In this paper, we introduce a multimodal learning to rank model that combines these traditional features with visual semantic features transferred from a deep convolutional neural network. In a large scale experiment using data from the online marketplace Etsy, we verify that moving to a multimodal representation significantly improves ranking quality. We show how image features can capture fine-grained style information not available in a text-only representation. In addition, we show concrete examples of how image information can successfully disentangle pairs of highly different items that are ranked similarly by a text-only model.
@article{arxiv.1511.06746,
title = {Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank},
author = {Corey Lynch and Kamelia Aryafar and Josh Attenberg},
journal= {arXiv preprint arXiv:1511.06746},
year = {2015}
}