Demystifying Core Ranking in Pinterest Image Search
Pinterest Image Search Engine helps hundreds of millions of users discover interesting content everyday. This motivates us to improve the image search quality by evolving our ranking techniques. In this work, we share how we practically design and deploy various ranking pipelines into Pinterest imag...
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Published in | arXiv.org |
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Main Author | |
Format | Paper |
Language | English |
Published |
Ithaca
Cornell University Library, arXiv.org
26.03.2018
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Subjects | |
Online Access | Get full text |
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Summary: | Pinterest Image Search Engine helps hundreds of millions of users discover interesting content everyday. This motivates us to improve the image search quality by evolving our ranking techniques. In this work, we share how we practically design and deploy various ranking pipelines into Pinterest image search ecosystem. Specifically, we focus on introducing our novel research and study on three aspects: training data, user/image featurization and ranking models. Extensive offline and online studies compared the performance of different models and demonstrated the efficiency and effectiveness of our final launched ranking models. |
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ISSN: | 2331-8422 |