Title: Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce,
Abstract: In this paper, we present a unified end-to-end approach to build a large
scale Visual Search and Recommendation system for e-commerce. Previous works
have targeted these problems in isolation. We believe a more effective and
elegant solution could be obtained by tackling them together. We propose a
unified Deep Convolutional Neural Network architecture, called VisNet, to learn
embeddings to capture the notion of visual similarity, across several semantic
granularities. We demonstrate the superiority of our approach for the task of
image retrieval, by comparing against the state-of-the-art on the Exact
Street2Shop dataset. We then share the design decisions and trade-offs made
while deploying the model to power Visual Recommendations across a catalog of
50M products, supporting 2K queries a second at Flipkart, India's largest
e-commerce company. The deployment of our solution has yielded a significant
business impact, as measured by the conversion-rate.