Learning to Rank with Deep Visual Semantic Features
Search is an important problem for modern e-commerce platforms such as Etsy. As a result, the task of ranking search results automatically or the so-called learning to rank is a multibillion dollar machine learning problem.In this talk, we first review Etsy's approach to learning to rank using a few hand-constructed features based on the Etsy listing's text-based representation.
We then discuss a multimodal learning to rank model that combines these traditional text-based features with visual semantic features transferred from a deep convolutional neural network. We show that a multimodal approach to learning to rank can improve the quality of ranking in an experimental setting.
Kamelia Aryafar, Ph.D. is a senior data scientist with Etsy's Data Science team since 2013. She works on building scalable machine learning and computer vision tools to curate a personalized experience for Etsy users. Prior to Etsy she was doing a Ph.D. in computer science and machine learning in Drexel University, building large-scale music classification models.