Abstract
In recent years, there has been a growing demand to develop efficient recommender systems which track users' preferences and recommend potential items of interest to users. In this talk, I will present a smooth collaborative recommender system to utilize dependency information among users and items which share similar characteristics under the singular value decomposition framework. The proposed method incorporates the neighborhood structure among user-item pairs by exploiting covariates to improve the prediction performance. One key advantage of the proposed method is that it leads to more effective recommendation for “cold-start” users and items, whose preference information is completely missing from the training set.

As this type of data involves large-scale customer records, efficient scheme will be proposed to achieve scalable computing. The advantage is confirmed in a variety of simulated experiments as well as one large-scale real example on Last.fm music listening counts. If time permits, the asymptotic properties will also be discussed.

Biography
Dr Junhui Wang is an Associate Professor of Department of Mathematics at CityU. His current research interests include statistical machine learning and data mining, analysis of large-scale unstructured data, and high-dimensional data. Prior to joining CityU in 2013, Dr Wang was Assistant/Associate Professor at University of Illinois at Chicago. He holds a bachelor degree and doctoral degree in Statistics from Peking University and University of Minnesota, respectively.