Monday April 21, 12pm-1pm, 1116-E Klaus
Local Collaborative Ranking
Advisor: Prof. Guy Lebanon
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 paper, 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.
Seungyeon Kim is a Ph.D. candidate at Georgia Tech and a student of SMLV lab under the supervision of Professor. Guy Lebanon. He received his bachelor's degree in Computer Science and Engineering from Seoul National University and master's degree in CS at Georgia Tech. He is working on machine learning while focusing effective document representations under various settings, such as labels or sequential information. Main applications of his approaches mostly lies on Natural Language Processing, for example sentiment/mood analysis, topic modeling, sequential document modeling. He also works in collaborative filtering which got best student paper in WWW2014.