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Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY
Longqi Yang

Longqi Yang
Graduate Student, Cornell Tech, Cornell University


Longqi Yang is a PhD candidate in computer science at Cornell Tech and Cornell University, where he is advised by Deborah Estrin, and is a member of the Connected Experiences Lab and the Small Data Lab. His current research focuses are user modeling, recommendation systems, and recommendation for social good. His work has been published and presented in top academic conferences, such as WWW, WSDM, Recsys, and CIKM. He co-organized workshops at the NYC Media Lab annual summit 2017 and KDD 2018.


1:15pm–1:55pm Wednesday, 09/12/2018
Location: 1A 15/16 Level: Intermediate
Secondary topics:  Deep Learning, Media, Marketing, Advertising, Recommendation Systems, Retail and e-commerce
Longqi Yang (Cornell Tech, Cornell University)
State-of-the-art recommendation algorithms are increasingly complex and no longer one size fits all. Current monolithic development practice poses significant challenges to rapid, iterative, and systematic, experimentation. Longqi Yang explains how to use OpenRec to easily customize state-of-the-art solutions for diverse scenarios. Read more.