主 题：A smooth collaborative recommender system
Prof Junhui Wang received his BSc in Probability and Statistics from Peking University, and PhD in Statistics from University of Minnesota. Before joining City University in 2013, he worked as associate professor at University of Illinois at Chicago. He currently serves as Associate Editor for Annals of the Institute of Statistical Mathematics, and Statistics and Its Interface.
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.
编辑：罗莎 / 审核：王晓刚 / 发布者：陈伟