Gedas Adomavicius

Gedas Adomavicius

Professor & Department Chair
Carolyn I. Anderson Chair in Business Education Excellence
CSOM Information/Decision Sciences

Contact

3-320 Carlson School of Management

Education:

  • BS 1995
    Mathematics Vilnius University
  • MS 1998
    Computer Science New York University
  • PhD 2002
    Computer Science New York University

Expertise:

  • Electronic Market Mechanisms
  • Data Mining and Knowledge Discovery
  • Personalization Technologies and Recommender Systems

About Gedas

Gedas Adomavicius is a professor in the Department of Information and Decision Sciences at the Carlson School of Management, University of Minnesota, where he also holds the Carolyn I. Anderson Chair in Business Education Excellence.  He received his PhD degree in computer science from New York University.  His general research interests revolve around computational techniques for aiding decision-making in information-intensive environments and include personalization technologies and recommender systems, machine learning and data analytics, and electronic market mechanisms.  His research has been published in a number of leading academic journals in information systems and computer science, including Information Systems Research, MIS Quarterly, Management Science, Journal of Operations Management, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, and Data Mining and Knowledge Discovery, and has been cited more than 18,000 times to date (according to Google Scholar).  He has received several research grants from major funding institutions, including the U.S. National Science Foundation CAREER award for his research on personalization technologies.  He has served on the editorial boards of several leading academic journals, including as Senior Editor for Information Systems Research and MIS Quarterly.  In 2017, Prof. Adomavicius received the INFORMS Information Systems Society’s Distinguished Fellow Award.  At the Carlson School of Management, he has taught analytics-related courses in the undergraduate, MBA, MSBA, PhD, and Executive Education programs and is currently serving as the chair of the Information and Decision Sciences Department.


Selected Works & Activities.

Browse some of my work and see what I’ve been up to.

  • G. Adomavicius, J. Bockstedt, S. Curley, and J. Zhang. Reducing Recommender Systems Biases: An Investigation of Rating Display Designs. MIS QUARTERLY. Forthcoming.
  • G. Adomavicius, A. Gupta, and M. Yang. Designing Real-Time Feedback for Bidders in Homogeneous-Item Continuous Combinatorial Auctions. MIS QUARTERLY. Forthcoming.
  • M. Yang, Y. Ren, and G. Adomavicius. Understanding User-Generated Content and Customer Engagement on Facebook Business Pages. INFORMATION SYSTEMS RESEARCH. Forthcoming.
  • M. Yang, G. Adomavicius, and A. Gupta. Efficient Computational Strategies for Dynamic Inventory Liquidation. INFORMATION SYSTEMS RESEARCH. Forthcoming.
  • G. Adomavicius, J. Bockstedt, S. Curley, J. Zhang, and S. Ransbotham. Hidden Side Effects of Recommendation Systems. MIT SLOAN MANAGEMENT REVIEW, 60(2):13-15, Winter 2019.
  • M. Yang, G. Adomavicius, G. Burtch, and Y. Ren. Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining. INFORMATION SYSTEMS RESEARCH, 29(1):4-24, March 2018.
  • G. Adomavicius, J. Bockstedt, S. Curley, and J. Zhang. Effects of Online Recommendations on Consumers’ Willingness to Pay. INFORMATION SYSTEMS RESEARCH, 29(1):84-102, March 2018.
  • J. Wolfson et al. Use and Customization of Risk Scores for Predicting Cardiovascular Events Using Electronic Health Record Data. JOURNAL OF THE AMERICAN HEART ASSOCIATION, 6(4), April 2017.
  • M. Bichler, Z. Hao, and G. Adomavicius. Coalition-Based Pricing in Ascending Combinatorial Auctions. INFORMATION SYSTEMS RESEARCH, 28(1):159-179, 2017.
  • A. Ermagun, Y. Fan, J. Wolfson, G. Adomavicius, and K. Das. Real-Time Trip Purpose Prediction Using Online Location-Based Search and Discovery Services. TRANSPORTATION RESEARCH PART C: EMERGING TECHNOLOGIES, 77:96-112, April 2017.
  • D.M. Vock et al. Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting. JOURNAL OF BIOMEDICAL INFORMATICS, 61:119-131, June 2016.
  • G. Adomavicius and J. Zhang. Classification, Ranking, and Top-K Stability of Recommendation Algorithms. INFORMS JOURNAL ON COMPUTING, 28(1):129-147, 2016.
  • J. Wolfson et al. A Naïve Bayes Machine Learning Approach to Risk Prediction Using Censored, Time-to-Event Data. STATISTICS IN MEDICINE, 34(21):2941-2957, 2015.
  • S. Bandyopadhyay et al. Data mining for censored time-to-event data: A Bayesian network model for predicting cardiovascular risk from electronic health record data. DATA MINING AND KNOWLEDGE DISCOVERY, 29(4):1033-1069, July 2015.
  • G. Adomavicius and J. Zhang. Improving Stability of Recommender Systems: A Meta-Algorithmic Approach. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 27(6):1573-1587, June 2015.
  • G. Adomavicius, J. Bockstedt, and S. Curley. Bundling Effects on Variety Seeking for Digital Information Goods. JOURNAL OF MANAGEMENT INFORMATION SYSTEMS, 31(4):182-212, 2015.
  • G. Meyer, G. Adomavicius, P. Johnson, M. Elidrisi, W. Rush, J. Sperl-Hillen, and P. O’Connor. A Machine Learning Approach to Improving Dynamic Decision Making. INFORMATION SYSTEMS RESEARCH, 25(2):239-263, 2014.
  • G. Adomavicius and Y. Kwon. Optimization-Based Approaches for Maximizing Aggregate Recommendation Diversity. INFORMS JOURNAL ON COMPUTING, 26(2):351-369, 2014.
  • G. Adomavicius, J. Bockstedt, S. Curley, and J. Zhang. Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects. INFORMATION SYSTEMS RESEARCH, 24(4):956-975, 2013.
  • G. Adomavicius, S. Curley, A. Gupta, and P. Sanyal. User Acceptance of Complex Electronic Market Mechanisms: Role of Information Feedback. JOURNAL OF OPERATIONS MANAGEMENT, 31(6):489-503, 2013.
  • G. Adomavicius, S. Curley, A. Gupta, and P. Sanyal. Impact of Information Feedback in Continuous Combinatorial Auctions: An Experimental Study of Economic Performance. MIS QUARTERLY, 37(1):55-76, March 2013.
  • G. Adomavicius and J. Zhang. Stability of Recommendation Algorithms. ACM TRANSACTIONS ON INFORMATION SYSTEMS, 30(4), Article 23 (31 pages), November 2012.
  • G. Adomavicius, J. Bockstedt, and A. Gupta. Modeling Supply-Side Dynamics of IT Components, Products, and Infrastructure: An Empirical Analysis Using Vector Autoregression. INFORMATION SYSTEMS RESEARCH, 23(2):397-417, 2012.
  • G. Adomavicius and Y. Kwon. Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 24(5):896-911, May 2012.
  • G. Adomavicius, S. Curley, A. Gupta, and P. Sanyal. Effect of Information Feedback on Bidder Behavior in Continuous Combinatorial Auctions. MANAGEMENT SCIENCE, 58(4):811-830, April 2012.
  • G. Adomavicius, A. Gupta, and P. Sanyal. Effect of Information Feedback on the Outcomes and Dynamics of Multisourcing Multiattribute Procurement Auctions. JOURNAL OF MANAGEMENT INFORMATION SYSTEMS, 28(4):199-229, Spring 2012.
  • G. Adomavicius and J. Zhang. Impact of Data Characteristics on Recommender Systems Performance. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS, 3(1), Article 3 (17 pages), April 2012.
  • G. Adomavicius, B. Mobasher, F. Ricci, and A. Tuzhilin. Context-Aware Recommender Systems. AI MAGAZINE. 32(3):67-80, 2011.
  • G. Adomavicius, A. Tuzhilin, and R. Zheng. REQUEST: A Query Language for Customizing Recommendations. INFORMATION SYSTEMS RESEARCH, 22(1):99-117, 2011.
  • G. Adomavicius, A. Gupta, and D. Zhdanov. Designing Intelligent Software Agents for Auctions with Limited Information Feedback. INFORMATION SYSTEMS RESEARCH, 20(4):507-526, 2009.
  • G. Adomavicius, J. Bockstedt, A. Gupta, and R. Kauffman. Making Sense of Technology Trends in the Information Technology Landscape: A Design Science Approach. MIS QUARTERLY, 32(4):779-810, 2008.
  • G. Adomavicius, J. Bockstedt, A. Gupta, and R. Kauffman. Understanding Evolution in Technology Ecosystems. COMMUNICATIONS OF THE ACM, 51(10):117-122, 2008.
  • G. Adomavicius and J. Bockstedt. C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multi-Attribute Transactional Data. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 20(6):721-735, June 2008.
  • G. Adomavicius and Y. Kwon. New Recommendation Techniques for Multi-Criteria Rating Systems. IEEE INTELLIGENT SYSTEMS, 22(3):48-55, May-June 2007.
  • G. Adomavicius, J. Bockstedt, A. Gupta, and R. Kauffman. Technology Roles and Paths of Influence in an Ecosystem Model of Technology Evolution. INFORMATION TECHNOLOGY AND MANAGEMENT, 8(2):185-202, June 2007.
  • G. Adomavicius and A. Tuzhilin. Validation Sequence Optimization: A Theoretical Approach. INFORMS JOURNAL ON COMPUTING, 19(2):185-200, 2007.
  • G. Adomavicius and A. Tuzhilin. Personalization Technologies: A Process-Oriented Perspective. COMMUNICATIONS OF THE ACM, 48(10):83-90, October 2005.
  • G. Adomavicius and A. Gupta. Towards Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions. INFORMATION SYSTEMS RESEARCH, 16(2):169-185, 2005.
  • G. Adomavicius and A. Tuzhilin. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 17(6):734-749, 2005.
  • G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin. Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach. ACM TRANSACTIONS ON INFORMATION SYSTEMS, 23(1):103-145, January 2005.
  • G. Adomavicius and A. Tuzhilin. An Architecture of e-Butler: A Consumer-Centric Online Personalization System. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2(3):313-327, 2002.
  • G. Adomavicius and A. Tuzhilin. Expert-Driven Validation of Rule-Based User Models in Personalization Applications. DATA MINING AND KNOWLEDGE DISCOVERY, 5(1/2):33-58, 2001.
  • G. Adomavicius and A. Tuzhilin. Using Data Mining Methods to Build Customer Profiles. IEEE COMPUTER, 34(2):74-82, 2001.
  • Current Research
    • Multidimensional recommender systems

    • Techniques for customer modeling

    • Real-time bidder support in complex auction mechanisms

    • Expert-driven validation of data mining results

    • Personalization process and user acceptance of personalization technologies

    • Ecosystem models of technology evolution

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