Powering Recommendations at Twitter: Overview and Algorithmic Details

Recommendations at Twitter span a number of applications, from user recommendations (who-to-follow) to content recommendations (push, email). A number of building blocks are essential for each recommendation pipeline, such as user-similarities, user interest models and tweet topic/entity recognition.

In this talk at Metis, Praveeen Bommanavar (http://praveenbom.com/), Data Scientist at Twitter, will begin with an overview of recommendations at Twitter and then zoom in on a methodology for inferring user interests to help power the aforementioned applications

Where:

Tuesday, September 1st from 6-8pm at Metis (http://www.thisismetis.com/)
27 East 28th Street, 3rd Floor, New York, NY

6:00 - 6:30 Registration, Food, Drinks and Networking
6:30 - 7:30 Presentation and Q&A
7:30 - 8:00 Food, Drinks, and Networking

Praveen Bommannavar

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Praveen Bommannavar (http://praveenbom.com/), Data Scientist at Twitter, received his PhD in Operations Research from Stanford and a BS and MS in Electrical Engineering from the University of Illinois at Urbana-Champaign. He has worked on data mining problems at a number of tech companies including Twitter, LinkedIn, Klout, and @WalmartLabs.