Visual Search at Pinterest
Yushi Jing, David Liu, Dmitry Kislyuk, Andrew Zhai and, Jiajing Xu, Jeff Donahue, Sarah Tavel

TL;DR
This paper shows how a small team can build and deploy a large-scale visual search system using cloud computing and open-source tools, and demonstrates its effectiveness in improving user engagement at Pinterest.
Contribution
It provides a practical implementation of a cost-effective, large-scale visual search system and shares insights from deploying it in a commercial setting.
Findings
Visual search improves user engagement at Pinterest.
Open-source tools enable scalable visual search deployment.
Cost-effective approach for small teams to implement visual search.
Abstract
We demonstrate that, with the availability of distributed computation platforms such as Amazon Web Services and open-source tools, it is possible for a small engineering team to build, launch and maintain a cost-effective, large-scale visual search system with widely available tools. We also demonstrate, through a comprehensive set of live experiments at Pinterest, that content recommendation powered by visual search improve user engagement. By sharing our implementation details and the experiences learned from launching a commercial visual search engines from scratch, we hope visual search are more widely incorporated into today's commercial applications.
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Taxonomy
TopicsVisual Attention and Saliency Detection · Advanced Image and Video Retrieval Techniques · Multimodal Machine Learning Applications
