Personalized Expert Discovery in Social Media
Department of Computer Science and Engineering
Texas A&M University
Tuesday, November 10, 2015
Brickyard (BYENG) 210, Tempe campus [map]
Experts are important for providing reliable and authoritative information and opinion, as well as for improving online reviews and services. While considerable previous research has focused on finding topical experts with broad appeal, this talk will highlight our efforts toward uncovering *personalized* experts who have special personal appeal and importance to users. One of the key insights motivating our approach is to leverage the geo-spatial preferences of users and the variation of these preferences across different regions, topics, and social communities.
James Caverlee is an associate professor in the Department of Computer Science and Engineering at Texas A&M University. His research focuses on web-scale information management, distributed data-intensive systems, and social computing. Most recently, he’s been working on (i) spam and crowdturfing threats to social media and web systems; and (ii) geo-social systems that leverage large-scale spatio-temporal footprints in social media. Caverlee is a recipient of the 2010 Defense Advanced Research Projects Agency (DARPA) Young Faculty Award, the 2012 Air Force Office of Scientific Research (AFOSR) Young Investigator Award, and a 2012 NSF CAREER Award. He received his Ph.D. from Georgia Tech in 2007, M.S. degrees in Computer Science (2001) and in Engineering-Economic Systems & Operations Research (2000) from Stanford University, and a B.A. in Economics from Duke University (1996, magna cum laude).