Industry Track

We are pleased to announce that we could gather experts from leading companies to describe their technologies at RecSys:

Eric Bieschke (Pandora): Pandora Playlists

Eric Bieschke runs playlist engineering for Pandora. As Pandora's second employee he built initial prototypes for Pandora's playlist algorithms and with his team has grown them to service more than 100M users who have thumbed 10 billion songs while listening to billions of hours of music. He is currently working on optimally combining content-based recommendations, collective intelligence, and human-machine cooperation in order to provide the best experience for listeners.

Pankaj Gupta (Twitter): Recommendations and Discovery at Twitter

Pankaj Gupta currently leads the user discovery team at Twitter. Most recently he led the design and implementation of the personalized recommendations system that suggests users to follow on Twitter. His interests are in systems and algorithms as applied to information retrieval, graph analysis and networking. Before Twitter, Pankaj was the co-founder of a music search startup. He was also the cofounder and CTO of Sahasra Networks before its acquisition by Cypress in 2002. Pankaj was the recipient of the Arthur Samuel award for the best Ph.D. dissertation in the Department of Computer Science at Stanford University in 2000-2001, and a medal winner for being the first in his undergraduate Computer Science class at IIT Delhi in 1995.

Andrew Tomkins (Google): Recommendation problems in Social Networks

Andrew Tomkins is an engineering director at Google working on measurement, modelling, and analysis of content, communities, and users on the World Wide Web. Prior to joining Google, he spent four years at Yahoo! as chief scientist of search, and eight years at IBM's Almaden Research Center, where he co-founded the WebFountain project. Andrew holds Bachelors degrees in Math and CS from MIT, and a PhD in CS from Carnegie Mellon University; he has published over a hundred technical papers.

Jon Sanders (Netflix): Suggestions to watch instantly

Jon Sanders leads an engineering team at Netflix, building the algorithms and systems that predict members' interest and enjoyment of each movie and TV show in the Netflix catalog. Jon was honored to host the Netflix Prize competition where thousands of teams broke new ground in large scale predictive algorithms. Translating innovations from this contest into commercial practice is one part of helping Netflix members to find something great to watch. Jon joined Netflix in 2007, having previously engineered enterprise software quality tools and electron beam semiconductor inspection equipment.

Rajat Raina (Facebook): Learning Relevance in a Large-scale Social Network

Rajat Raina is a research scientist at Facebook, where he focuses on large-scale applications of machine learning. He currently works on improving the relevance of online advertising. Every month, users spend 700 billion minutes on Facebook. Surfacing personalized relevant content to each user at this scale, and for each of the billions of page views, presents interesting research challenges. Rajat will talk about some such relevance models used at Facebook. As a sample application for matching content to user interests, he will describe a method for improving the relevance of online advertising.