Gedas Adomavicius is an associate professor in the Department of Information and Decision Sciences at the Carlson School of Management, University of Minnesota. His general research interests revolve around computational techniques for aiding decision-making in information-intensive environments and include personalization technologies, knowledge discovery and data mining, and electronic market mechanisms. His current research deals with next generation recommender systems and real-time bidder support in complex auction mechanisms. He has published in several leading academic journals, including "Management Science", "Information Systems Research", "Management Information Systems Quarterly", "IEEE Transactions on Knowledge and Data Engineering", "ACM Transactions on Information Systems", and "Data Mining and Knowledge Discovery". He received the National Science Foundation CAREER award in 2006 for his research on personalization technologies. He currently serves on the editorial boards of "Information Systems Research" and "INFORMS Journal on Computing". At the Carlson School, he teaches in the undergraduate, MBA, and PhD programs.
"Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions," G. Adomavicius and A. Tuzhilin, IEEE Transactions on Knowledge and Data Engineering (2005).
"Personalization Technologies: A Process-Oriented Perspective," G. Adomavicius and A. Tuzhilin, Communications of the ACM (2005).
"Towards Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions," G. Adomavicius and A. Gupta, Information Systems Research (2005).
"Incorporating Contextual Information in Recommender Systems Using a Multidemensional Approach," G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin, ACM Transactions on Information Systems (2005).
"Validation Sequence Optimization: A Theoretical Approach," G. Adomavicius and A. Tuzhilin, INFORMS Journal on Computing (forthcoming).
"Technology Roles and Paths of Influence in an Ecosystem Model of Technology Evolution," G. Adomavicius, J. Bockstedt, A. Gupta, and R. Kauffman, Information Technology and Management (forthcoming 2007).
Multidimensional recommender systems
Real-time bidder support in complex auction mechanisms
Expert-driven validation of data mining results
Techniques for customer modeling
Personalization process and user acceptance of personalization technologies
Ecosystem models of technology evolution