Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce
This addresses the need for integrated visual systems in large-scale e-commerce, though it is incremental by combining existing tasks.
The paper tackles the problem of visual search and recommendation for e-commerce by proposing a unified deep learning approach, resulting in deployment at Flipkart with support for 50M products and 2K queries per second, leading to improved conversion rates.
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.