个人简介：

卫强博士是清华大学经济管理学院管理科学与工程系担任信息管理副教授，主要研究和教学兴趣包括信息系统与信息管理，电子商务，商务智能与数据挖掘，社会网络与社会商务，管理模拟，数据库应用等领域。在ACM Transactions on Knowledge Discovery from Data (TKDD) 和ICIS等重要国际学术期刊和重要国际会议中发表了50余篇学术论文，编著两部教育部“十一五规划教材”《管理系统模拟》与《商务智能原理与方法》，其中《商务智能原理与方法》获得2011年教育部普通高等教育精品教材奖。担任如ACM Social and Economic Computing Chapter的Treasurer，中国管理科学与工程学会副秘书长、中国系统工程学会青年委员会委员、中国模糊数学与模糊系统学会理事等。参与组织多个重要国际会议，如IEEE-ICEBE2005组委会主席、ICEBI2010组委会副主席等。获教育部新世纪优秀人才（2012），教育部百篇优秀博士论文提名奖（2004），清华大学优秀博士毕业论文奖（2003），SAP Seed Fellowship（2001）等。

报告内容：

Competitiveness degree analysis is a focal point of business strategy and competitive intelligence, aimed to help managers closely monitor to what extent their rivals are competing with them. This article proposes a novel method, namely BCQ, to measure the competitiveness degree between peers from query logs as an important form of user generated contents, which reflects the “wisdom of crowds” from the search engine users’ perspective. In doing so, a bipartite graph model is developed to capture the competitive relationships through conjoint attributes hidden in query logs, where the notion of competitiveness degree for entity pairs is introduced, and then used to identify the competitive paths mapped in the bipartite graph. Subsequently, extensive experiments are conducted to demonstrate the effectiveness of BCQ to quantify the competitiveness degrees. Experimental results reveal that BCQ can well support competitors ranking, which is helpful for devising competitive strategies and pursuing market performance. In addition, efficiency experiments on synthetic data show a good scalability of BCQ on large scale of query logs.