Machine Learning at Pinterest
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Machine learning is at the core of Pinterest. Pinterest personalizes and ranks 1B+ pins, 700+ million boards for 100M+ users all over the world, using data gathered from collaborative filtering, user curation, web crawling, and more. At Pinterest we model relationships between pins, handle cold-start problems and deal with real-time recommendations.
In this talk Jure will give an overview of the problems and effective solutions developed at Pinterest. We will focus on systems and effective engineering choices made to enable productive machine learning development and enable multiple engineers effectively develop, test, and deploy machine-learned models.
Meet the Speaker:
Jure Leskovec (https://www.linkedin.com/in/leskovec), Chief Scientist at Pinterest and Assist. Professor at Stanford University
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Jure is Assistant Professor of Computer Science (https://cs.stanford.edu/people/jure/#) at Stanford University. His research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Problems he investigates are motivated by large scale data, the Web and on-line media.
He's also Chief Scientists at Pinterest, where he's focusing on machine learning problems. He co-founded a machine learning startup Kosei (https://www.crunchbase.com/organization/kosei), which was co-created by The Hive (http://hivedata.com) and was acquired by Pinterest in 2015.
In 2008/09 he was a postdoctoral researcher at Cornell University working with Jon Kleinberg (http://www.cs.cornell.edu/home/kleinber/) and Dan Huttenlocher (http://www.cs.cornell.edu/~dph/). He completed his Ph.D. in Machine Learning Department, School of Computer Science at Carnegie Mellon University under the supervision of Christos Faloutsos (http://www.cs.cmu.edu/~christos/) in 2008.
Agenda:
6:00 - 6:30 - Registration and Networking
6:30 - 6:45 - Introduction by The Hive
6:45 - 7:30 - Talk by Jure Leskovec
7:30 - 8:00 - Q&A
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