主 题: Smooth collaborative recommender systems
报告人: Junhui Wang (City University of Hong Kong)
时 间: 2018-05-24 14:00-15:00
地 点: Room 1303, Sciences Building No. 1
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.
About the speaker: Junhui Wang is now professor and associate head of Department of Mathematics at City University of Hong Kong. He received his B.S. in Probability and Statistics from Peking University in 2001, and Ph.D. in Statistics from University of Minnesota in 2006. His research interests include statistical machine learning, unstructured data analysis, big data analysis, model selection and variable selection, as well as their applications in biomedicine, finance and information technology. He has published 40+ research articles on leading statistics and machine learning journals, including 10+ on Journal of American Statistical Association, Biometrika, and Journal of Machine Learning Research. He also serves as associate editor of Annals of the Institute of Statistical Mathematics and Statistics and its interface.