User Modeling and Recommendations

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

Pinterest is one of the largest-scale recommender systems around, serving serve more than 10 billion recommendations every day. By combining the data we've amassed over the years with human curation, we've built human-centered personalization engines that can serve the right recommendation to the right person at the right moment, choosing from a pool of over 100 billion objects—all in real time.

Meet the team

Stephanie Rogers
Pong Eksombatchai
Jerry Liu
Charles Sugnet
Yuchen Liu
Pranav Jindal
Jure Leskovec
Ruining He
Aditya Pal
Rajat Raina

Publications
  • 1. 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.
  • 2. Understanding Online Collection Growth Over Time: A Case Study of Pinterest

    Team: Caroline Lo, Justin Cheng, Jure Leskovec
    When: ACM International Conference on World Wide Web (WWW), 2017.
  • 3. Predicting Intent Using Activity Logs: How Goal Specificity and Temporal Range Affect User Behavior

    Team: Justin Cheng, Caroline Lo, Jure Leskovec
    When: ACM International Conference on World Wide Web (WWW), 2017.
  • 4. Item-to-Item Recommendations at Pinterest

    Team: Stephanie Rogers
    When: ACM Conference on Recommender Systems (RecSys), 2016.
  • 5. Power of Human Curation in Recommendation System

    Team: Yuchen Liu, Dmitry Chechik, Junghoo Cho
    When: ACM International Conference on World Wide Web (WWW), 2016.

Blog