In this paper, we present a uni ed end-to-end approach to build a large scale Visual Search and Recommendation system for ecommerce. Previous works have targeted these problems in isolation. We believe a more e ective and elegant solution could be obtained by tackling them together. We propose a uni ed Deep Convolutional Neural Network architecture, called VisNet 1 , 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 [14] dataset. We then share the design decisions and trade-o s 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. e deployment of our solution has yielded a signi cant business impact, as measured by the conversion-rate.
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