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

arXiv: https://arxiv.org/abs/1703.02344 · HTML: https://mlsanity.com/paper/1703.02344/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2017.

## Built on by (most cited first, 16 of 16)

- [Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning](https://mlsanity.com/paper/2104.02429/) (2021)
- [ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval](https://mlsanity.com/paper/2505.20764/) (2025)
- [Towards Zero-shot Cross-lingual Image Retrieval and Tagging](https://mlsanity.com/paper/2109.07622/) (2021)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond](https://mlsanity.com/paper/2308.14753/) (2023)
- [FashionSearchNet-v2: Learning Attribute Representations with Localization for Image Retrieval with Attribute Manipulation](https://mlsanity.com/paper/2111.14145/) (2021)
- [Visually Similar Products Retrieval for Shopsy](https://mlsanity.com/paper/2210.04560/) (2022)
- [Are They the Same Picture? Adapting Concept Bottleneck Models for Human-AI Collaboration in Image Retrieval](https://mlsanity.com/paper/2407.08908/) (2024)
- [Understanding Endogenous Data Drift in Adaptive Models with Recourse-Seeking Users](https://mlsanity.com/paper/2503.09658/) (2025)
- [Single-Item Fashion Recommender: Towards Cross-Domain Recommendations](https://mlsanity.com/paper/2111.00758/) (2021)
- [Scaling Cross-Domain Content-Based Image Retrieval for E-commerce Snap and Search Application](https://mlsanity.com/paper/2204.11593/) (2022)
- [ITEm: Unsupervised Image-Text Embedding Learning for eCommerce](https://mlsanity.com/paper/2311.02084/) (2023)
- [Text-Based Product Matching -- Semi-Supervised Clustering Approach](https://mlsanity.com/paper/2402.10091/) (2024)
- [Deep Learning for Prediction and Classifying the Dynamical behaviour of Piecewise Smooth Maps](https://mlsanity.com/paper/2406.17001/) (2024)
- [Enhancing Supervised Composed Image Retrieval via Reasoning-Augmented Representation Engineering](https://mlsanity.com/paper/2508.11272/) (2025)
- [Graded-Relevance Composed Multimodal Retrieval for E-commerce Visual Search at Scale](https://mlsanity.com/paper/2609.24152/) (2026)

## Abstract (arXiv)

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.