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Visual Discovery at Pinterest

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arxiv 1702.04680 v2 pith:T32ORDML submitted 2017-02-15 cs.CV

Andrew Zhai , Dmitry Kislyuk , Yushi Jing , Michael Feng , Eric Tzeng , Jeff Donahue , Yue Li Du , Trevor Darrell This is my paper · ORCID
classification cs.CV
keywords visualdiscoveryenginepinterestrecommendationsearchservicesbehind
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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  1. Learning a Unified Embedding for Visual Search at Pinterest

    cs.CV 2019-08 conditional novelty 5.0 of 10

    A single multi-task metric learning embedding trained on engagement and human-labeled data outperformed Pinterest's three specialized visual search embeddings in offline, human, and A/B evaluations.

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