Industry Session 2
Date: Sunday, Sept 18, 2016, 14:00-15:40
Location: Stratton Student Center (Sala 202)
Chair: Wei Chai
Recommending for the World
by Justin Basilico & Yves Raimond (Netflix)
The Netflix experience is driven by a number of recommendation algorithms: personalized ranking, page generation, similarity, ratings, search, etc. On the January 6th we simultaneously launched Netflix in 130 new countries around the world, which brought the total to over 190 countries. Preparing for such a rapid expansion while ensuring each algorithm was ready to work seamlessly created new challenges for our recommendation and search teams. In this talk, we will highlight the four most interesting challenges we encountered in making our algorithms operate globally and how this improved our ability to connect members worldwide with stories they’ll love.
About the Speakers
Justin Basilico is a Research/Engineering Manager for Page Algorithms Engineering at Netflix. He leads an applied research team focused on developing the next generation of algorithms used to generate the Netflix homepage through machine learning, ranking, recommendation, and large-scale software engineering. He has also developed machine learning approaches that yielded significant improvements in the personalized ranking algorithms that drive the Netflix recommendation system. Prior to Netflix, he worked on machine learning in the Cognitive Systems group at Sandia National Laboratories. He is also the co-creator of the Cognitive Foundry, an open-source software library for building machine learning algorithms and applications. He grew up in Boston, did his undergrad at Pomona College, and started doing research in Recommender Systems in 2002 while in graduate school at Brown University.
Yves Raimond is a Research Manager at Netflix, where he leads the Search & Recommendation Algorithm Engineering team: a mixed team of researchers and engineers building the next generation of Machine Learning algorithms used to drive the Netflix experience. Before that, he was a Lead Research Engineer in BBC R&D, working on information extraction from Multimedia content. He holds a PhD from Queen Mary, University of London.