Machine Learning

Pinterest Labs tackles the most challenging problems in Machine Learning and Artificial Intelligence
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Machine Learning at Pinterest

Tens of millions of people interact with Pinterest each day, browsing, searching and discovering ideas inspired by their tastes. To help make it all happen, Pinterest’s researchers and engineers build new computer vision models that “see” the content of each Pin, filtering abusive and misleading content, optimize ad placements and ranking nearly 300 billion Pins daily.

Meet the team

Mukund Narasimhan
Sonja Knoll
Ruben Ortega
Kevin Ma
Yunsong Guo
David Liu
Derek Cheng
Jiajing Xu
Xiaofang Chen
Vanja Josifovski
Jure Leskovec
Ruining He

Publications
  • 1. Visual Discovery at Pinterest

    Team: Andrew Zhai, Dmitry Kislyuk, Yushi Jing, Michael Feng, Eric Tzeng, Jeff Donahue, Yue Li Du, Trevor Darrell
    When: ACM International Conference on World Wide Web (WWW), 2017.
  • 2. Related Pins at Pinterest: The Evolution of a Real-World Recommender System

    Team: David C. Liu, Stephanie Rogers, Raymond Shiau, Dmitry Kislyuk, Kevin C. Ma, Zhigang Zhong, Jenny Liu, Yushi Jing
    When: ACM International Conference on World Wide Web (WWW), 2017.
  • 3. Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images

    Team: Junhua Mao, Jiajing Xu, Kevin Jing, Alan L. Yuille
    When: Advances in Neural Information Processing Systems (NIPS), 2016.
  • 4. Understanding Online Collection Growth Over Time: A Case Study of Pinterest

    Team: Julian McAuley, Rahul Pandey, Jure Leskovec
    When: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2015.
  • 5. Human curation and covnets; Powering Item-to-Item Recommendations on Pinterest

    TeaDmitry Kislyuk, Yuchen Liu, David Liu, Eric Tzeng, Yushi Jing
    When: Arxiv, 2015.

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