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.
@article{arxiv.1505.07647,
title = {Visual Search at Pinterest},
author = {Yushi Jing and David Liu and Dmitry Kislyuk and Andrew Zhai and Jiajing Xu and Jeff Donahue and Sarah Tavel},
journal= {arXiv preprint arXiv:1505.07647},
year = {2017}
}
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in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge and Discovery and Data Mining, 2015