题目：A smooth collaborative recommender system
报告人：Prof. Junhui Wang（School of Data Science and Department of Mathematics ，City University of Hong Kong）
摘要：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 decom-position framework. The proposed method incorporates the neighborhood structure among user-item pairs by exploiting covariates to improve the pre-diction 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.
Dr. Junhui Wang is Professor in the School of Data Science and Department of Mathematics at City University of Hong Kong. He received his B.S. in Probability and Statistics from Peking University, and Ph.D. in Statistics from University of Minnesota. His research interests include statistical machine learning, unstructured data analysis, model selection and variable selection, as well as their applications in biomedicine, finance and information technology. He has published research articles on leading statistics and machine learning journals, and he also serves as Associate Editor of Annals of the Institute of Statistical Mathematics and Statistics and its interface.