Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce

TL;DR

This paper introduces VisNet, a unified deep learning architecture for large-scale visual search and recommendation in e-commerce, demonstrating improved image retrieval and significant business impact at Flipkart.

Contribution

The paper presents a novel unified CNN model that simultaneously addresses visual search and recommendation, optimized for large-scale deployment in e-commerce.

Findings

Superior image retrieval performance on Exact Street2Shop dataset

Supports 2,000 queries per second at Flipkart

Achieved significant increase in conversion rate

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.