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arxiv: 1803.09799 · v1 · pith:RMQPHOHHnew · submitted 2018-03-26 · 💻 cs.IR

Demystifying Core Ranking in Pinterest Image Search

classification 💻 cs.IR
keywords imagerankingsearchmodelspinterestaspectscomparedcontent
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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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  1. Effects of Foraging in Personalized Content-based Image Recommendation

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    Applies Information Foraging Theory to demonstrate that visual bookmarks increase the scent of recommended images in a content-based image recommender evaluated on Pinterest.