四川大学“智慧法治系列讲座”第10期| A smooth collaborative recommender system

主   题： A smooth collaborative recommender system

报告人： 王军辉（香港城市大学教授）

主持人：翁洋（四川大学数学学院副教授）

时  间：5月10日（周五）上午10：00-11:30

地  点：四川大学数学学院西303会议室

报告人简介：Prof. Junhui Wang received his BSc from Peking University and PhD from University of Minnesota. Before joining CityU, he was Associate professor at University of Illinois at Chicago. His major research interests include statistical machine learning and data mining, unstructured data analysis, high-dimensional data analysis, and applications in engineering, finance and biomedical sciences.

内容提要: 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 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. 

四川大学“智慧法治”超前部署学科

2019年5月6日