# Visual Discovery at Pinterest

arXiv: https://arxiv.org/abs/1702.04680 · HTML: https://mlsanity.com/paper/1702.04680/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2017.

## Built on by (most cited first, 11 of 11)

- [Online Matching: A Real-time Bandit System for Large-scale Recommendations](https://mlsanity.com/paper/2307.15893/) (2023)
- [Visually Similar Products Retrieval for Shopsy](https://mlsanity.com/paper/2210.04560/) (2022)
- [Efficient Large-Scale Visual Representation Learning And Evaluation](https://mlsanity.com/paper/2305.13399/) (2023)
- [Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Framework](https://mlsanity.com/paper/2306.07992/) (2023)
- [Learning with Multi-modal Gradient Attention for Explainable Composed Image Retrieval](https://mlsanity.com/paper/2308.16649/) (2023)
- [Virtual ID Discovery from E-commerce Media at Alibaba: Exploiting Richness of User Click Behavior for Visual Search Relevance](https://mlsanity.com/paper/2102.04667/) (2021)
- [Web-Scale Generic Object Detection at Microsoft Bing](https://mlsanity.com/paper/2107.01814/) (2021)
- [MetaCon: Unified Predictive Segments System with Trillion Concept Meta-Learning](https://mlsanity.com/paper/2203.04540/) (2022)
- [ICAR: Image-based Complementary Auto Reasoning](https://mlsanity.com/paper/2308.09119/) (2023)
- [Applying Deep Learning to Ads Conversion Prediction in Last Mile Delivery Marketplace](https://mlsanity.com/paper/2502.10514/) (2025)
- [PinPoint: Evaluation of Composed Image Retrieval with Explicit Negatives, Multi-Image Queries, and Paraphrase Testing](https://mlsanity.com/paper/2603.04598/) (2026)

## Abstract (arXiv)

Over the past three years Pinterest has experimented with several visual search and recommendation services, including Related Pins (2014), Similar Looks (2015), Flashlight (2016) and Lens (2017). This paper presents an overview of our visual discovery engine powering these services, and shares the rationales behind our technical and product decisions such as the use of object detection and interactive user interfaces. We conclude that this visual discovery engine significantly improves engagement in both search and recommendation tasks.