学术讲座:A smooth collaborative recommender system

文:统计机器智能与学习实验室 图:统计机器智能与学习实验室 / 来源:计算机学院 / 2019-05-08 / 点击量:2381

  计算机科学与工程学院特别邀请香港城市大学王军辉教授,与我校师生分享他的研究心得。具体安排如下,欢迎感兴趣的师生参加:

  主 题:A smooth collaborative recommender system

  时 间:2019年5月13日(周一)10:00-12:00

  地 点:清水河校区主楼B1-104会议室

  主讲人:王军辉博士 香港城市大学教授

 19_MA_WANG_Junhui_Photo.jpg

  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.

  报告内容:

  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.

 

                 计算机科学与工程学院(网络空间安全学院)

                       2019年5月8日


编辑:罗莎  / 审核:王晓刚  / 发布者:陈伟