報告題目： Point-of-Interest Recommendations in Location-based Social Networks
報告人： 葛永博士，助理教授
單位：美國亞利桑那大學(xué)Eller管理學(xué)院
報告時間： 2016年5月25日（周三）10:00-11:30
報告地點： 逸夫樓508會議室
報告人簡介：葛永博士于分別于2013、2008、2005年在羅格斯大學(xué)(Rutgers University)、中國科技大學(xué)、西安交通大學(xué)獲得博士、碩士和學(xué)士學(xué)位。葛博士現(xiàn)工作于美國亞利桑那大學(xué)Eller管理學(xué)院，長期致力于數(shù)據(jù)挖掘、社交網(wǎng)絡(luò)的研究。葛博士在研究領(lǐng)域取得了優(yōu)秀的學(xué)術(shù)成果，在國際頂級期刊IEEE TKDE、ACM TOIS、ACM TKDD、ACM TIST和國際頂級學(xué)術(shù)會議SIGKDD、ICDM等發(fā)表論文五十余篇。在2011年獲ICDM最佳研究論文獎，2013年羅格斯大學(xué)商學(xué)院最佳學(xué)術(shù)研究獎，2012年羅格斯大學(xué)學(xué)位論文獎。葛永博士還是SIGKDD和ICDM等會議的程序委員會委員，TKDE、TIST、KAIS和TSMC-B等學(xué)術(shù)期刊審稿人。
報告摘要：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.
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