Pinterest is a catalog of the world’s ideas grounded in a content ecosystem of over 2B unique pins pinned to over 100B boards. To match users with this content, Pinterest has built a series of highly scalable recommender systems that have progressively allowed it to provide more and more personalized content. In 2017, the company completed its latest evolutionary step by migrating from a GBDT-based system to one using neural networks to predict various dimensions of user behavior.
Xiaofang Chen and Derek Cheng explore Pinterest’s recent transition to a TensorFlow-based system, covering the challenges and solutions to providing recommendations to over 160M monthly active users. While transitions like this can be frustrating for the teams undertaking them (and difficult for managers to justify), Xiaofang and Derek provide context to teams thinking about tackling a project like this so that they won’t be surprised by these gotchas and show how large gains are possible with a more flexible framework.
Xiaofang Chen is a software engineer at Pinterest working on home feed ranking. Previously, Xiaofang was a software developer at Amazon. She holds a PhD in computer science from the University of Utah.
Derek Zhiyuan Cheng is software engineer on the discovery team at Pinterest, where he builds large-scale machine learning models and features to improve Pinterest’s personalization recommendation systems. Previously, he worked at Google Research, where he helped improve personalized search and recommendation systems for Google Play, News, and Google Plus. Derek has authored over 20 peer-reviewed articles published in prestigious conferences and journals for applied machine learning, information retrieval, and data mining. He holds a PhD with a focus on geosocial data mining from Texas A&M University.
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