Recent Seminars and Conferences



What's Popular Amongst Your Friends?

Speaker:	Prof. Onkar Dabeer(TIFR)
Seminar Details:	Recommendation systems, like those employed by Netflix and Amazon, suggest relevant content to potential buyers. Given the lack of good statistical models and the massive datasets, the dominant research focus has been on proposing algorithms and testing their scalability and performance on specific data sets. Recently, however, there is emerging interest in provably good techniques. For example, several authors have taken a compressed sensing view of this problem, and derived bounds on the least number of samples required for matrix completion. In earlier work (with S. Aditya and B. Dey) we proposed a new viewpoint motivated by channel coding. In this talk, we further exploit this viewpoint to analyze a simple algorithm motivated by practice. The algorithm considered makes recommendations based on popularity amongst "like-minded" users. The algorithm has competitive performance on the Movielens and Netflix data sets, and for a mathematical model, we provide a detailed bit-error-rate (BER) analysis. In particular, the analysis reveals three distinct performance regimes, and gives insight into the performance of the scheme on real datasets
Date and Time:	Friday 17 March 3:30pm
Venue:	EEG-001, GG building