报告时间：2016年4月13日（星期三），下午14:00点
报告地点：浙江大学紫金港校区蒙民伟楼CAD&CG国家重点实验室402室
报告题目：Big Data on User Modeling and Personalized Recommendation in Geo-social Networks
报告人：王浩 副研究员
主持人：巫英才 研究员
Abstract: In this talk, I will begin by introducing our group (Big Data Intelligence, BDI) and relevant research topics. Then I will mainly introduce our latest work on Geo-social Networks Mining. With the rapid development of social media and mobile technology, location-based services and social networks begin to converge, resulting in the emergence of Geo-social networks. Geographic location bridges the gap between online virtual society and physical world. Unlike traditional online social networks and GPS trajectories, user behaviors in Geo-social networks are highly distributed so that low-sampling and sparse user data bring a huge challenge for current user modeling methods. Driven by multi-context information, i.e., time, space, content and social, this talk will systematically discuss some theories and methods for multi-level (i.e., individual level, tie-level, group-level) user modeling, which can be widely used in intelligent recommendation, abnormal behavior identification and users’ privacy protection.
Bio: Dr. Hao Wang works as an associate professor in Institute of Software, Chinese Academy of Sciences, leading a group of Big Data Intelligence (BDI). He received his doctoral degree from The University of Tokyo, and jointly cultivated by University of California, Berkeley. His current main research interests include data mining and machine learning, knowledge and chance discovery, user profiling and behavior modeling, knowledge-based and intelligent system, topic discovery and event detection. He has published over 30 papers, and some of them have been published in reputed journals and top international conferences including AAAI, IJCAI, AAMAS, ICDM, ICDE, CIKM, HICSS, SMC, ESWA and KBS.
