报告人简介：
马宝君，博士，北京邮电大学经济威廉希尔副教授，比利时鲁汶大学、香港城市大学访问学者。2007年7月和2013年7月在清华大学先后获得学士和博士学位，主持1项国家自然科学基金青年项目，参与多项国家自然科学基金重大、重点、面上及青年项目，目前发表学术论文20余篇，其中SSCI/SCI索引8篇，合作出版1本“十二五”普通高等教育本科国家级规划教材，担任多个国际SSCI/SCI学术期刊、国内学术期刊特邀评审专家，多个国际学术会议的大会程序委员会委员及分论坛主席。主要教学与研究领域包括商务智能与数据挖掘、电子商务与信息搜索服务、移动用户行为大数据分析以及政策信息学等。
报告简介：
Recent years have witnessed a rapid increase in online data volume and the growing challenge of information overload for web use and applications. Thus, information diversity is of great importance to both information service providers and users of search services. Based on a diversity evaluation measure (namely, information coverage), a heuristic method, namely FastCovC+S-Select, with corresponding algorithms is designed upon the greedy submodular idea. First, we devise the CovC+S-Select algorithm, which possesses the characteristic of asymptotic optimality, to optimize information coverage using a strategy in spirit of simulated annealing. To accelerate the efficiency of CovC+S-Select, its fast approximation (i.e., FastCovC+S-Select) is then developed through a heuristic strategy to downsize the solution space with the properties of information coverage. Furthermore, ample experiments have been conducted to show the effectiveness, efficiency and parameter robustness of the proposed method, along with comparative analyses revealing the performances advantageous over other related methods.
