报告题目： Point-of-Interest Recommendations in Location-based Social Networks

报告人： 葛永博士，助理教授

单位：美国亚利桑那大学Eller管理学院

报告时间： 2016年5月25日（周三）10:00-11:30

报告地点： 逸夫楼508会议室

报告人简介：葛永博士于分别于2013、2008、2005年在罗格斯大学(Rutgers University)、中国科技大学、西安交通大学获得博士、硕士和学士学位。葛博士现工作于美国亚利桑那大学Eller管理学院，长期致力于数据挖掘、社交网络的研究。葛博士在研究领域取得了优秀的学术成果，在国际顶级期刊IEEE TKDE、ACM TOIS、ACM TKDD、ACM TIST和国际顶级学术会议SIGKDD、ICDM等发表论文五十余篇。在2011年获ICDM最佳研究论文奖，2013年罗格斯大学商学院最佳学术研究奖，2012年罗格斯大学学位论文奖。葛永博士还是SIGKDD和ICDM等会议的程序委员会委员，TKDE、TIST、KAIS和TSMC-B等学术期刊审稿人。

报告摘要：With the rapid development of Location-based Social Network (LBSN) services, a large number of Point-Of-Interests (POIs) have been available, which consequently raises a great demand of building personalized POI recommender systems. A personalized POI recommender system can significantly assist users to find their preferred POIs and help POI owners to attract more customers. However, it is very challenging to develop a personalized POI recommender system because a user's check-in decision making process is very complex and could be influenced by many factors such as social network, geographical position, and the dynamics of user preferences. In this talk, we propose to divide the whole recommendation space into two parts: social friend space and user interest space. The social friend space denotes the set of POI candidates that users' friends have checked-in before, and the user interest space refers to the set of POI candidates that are similar to users' historical check-ins, but are not visited by their friends yet. To evaluate the proposed methods, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on the real-world data sets.

太阳集团tyc5997