Speaker: Joonseok Lee, Georgia Tech
Title: Local Collaborative Ranking for Recommendation Systems
Personalized recommendation systems are used in a wide variety of applications such as electronic commerce, social networks, web search, and more. Collaborative filtering approaches to recommendation systems typically assume that the rating matrix (e.g., movie ratings by viewers) is low-rank. In this talk, we examine an alternative approach in which the rating matrix is locally low-rank. Concretely, we assume that the rating matrix is low-rank within certain neighborhoods of the metric space defined by (user, item) pairs. We combine a recent approach for local low-rank approximation based on the Frobenius norm with a general empirical risk minimization for ranking losses. Our experiments indicate that the combination of a mixture of local low-rank matrices each of which was trained to minimize a ranking loss outperforms many of the currently used state-of-the-art recommendation systems. Moreover, our method is easy to parallelize, making it a viable approach for large scale real-world rank-based recommendation systems.
Joonseok Lee is a Ph.D candidate in the College of Computing at the Georgia Institute of Technology. He is mainly working on recommendation systems and collaborative filtering in the Statistical Machine Learning and Visualization Lab. He has done three internships during his Ph.D, including Amazon (2014 Summer), Microsoft Research (2014 Spring), and Google (2013 Summer). Before coming to Georgia Tech, he worked in NHN corp. in Korea (2007-2010). He received his B.S degree in computer science and engineering from Seoul National University, Korea. His paper "Local Collaborative Ranking" received the best student paper award from the 23rd International World Wide Web Conference (2014).