Gediminas Adomavicius

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Understanding Patterns of Technology Evolution: An Ecosystem Perspective

Robert J. Kauffman, Alok Gupta, Gediminas Adomavicius, 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

Gediminas Adomavicius, YoungOk Kwon, YoungOk Kwon, 2012, IEEE Transactions on Knowledge and Data Engineering.

Towards more confident recommendations: Improving recommender systems using filtering approach based on rating variance

Gediminas Adomavicius, YoungOk Kwon, Sreeharsha Kamireddy, 2007 .

Letter to the editor [6]

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2006 .

Optimization-Based Approaches for Maximizing Aggregate Recommendation Diversity

Gediminas Adomavicius, YoungOk Kwon, YoungOk Kwon, 2014, INFORMS J. Comput..

Workshop on context-aware recommender systems

Gediminas Adomavicius, Bamshad Mobasher, Moshe Unger, 2019, RecSys.

Understanding Effects of Personalized vs. Aggregate Ratings on User Preferences

Shawn P. Curley, Gediminas Adomavicius, Jesse C. Bockstedt, 2016, IntRS@RecSys.

Toward more diverse recommendations

Gediminas Adomavicius, Young Ok Kwon, Gediminas Adomavicius, 2009, WITS 2009.

A machine learning approach to improving process control

Gediminas Adomavicius, Paul E. Johnson, Patrick J. O'Connor, 2009, WITS 2009.

On the stability of recommendation algorithms

Gediminas Adomavicius, Jingjing Zhang, Jingjing Zhang, 2010, RecSys '10.

Real-time trip purpose prediction using online location-based search and discovery services

Yingling Fan, Gediminas Adomavicius, Alireza Ermagun, 2017 .

A visual mapping approach for trend identification in multi-attribute data

Gediminas Adomavicius, Jesse C. Bockstedt, 2007 .

How Decision Complexity Affects Outcomes in Combinatorial Auctions

Shawn P. Curley, Gediminas Adomavicius, Shawn P. Curley, 2020 .

Beyond Personalization: Research Directions in Multistakeholder Recommendation pdf

Dietmar Jannach, Gediminas Adomavicius, Robin Burke, 2019, ArXiv.

Workshop on Context-Aware Recommender Systems

Francesco Ricci, Gediminas Adomavicius, Bamshad Mobasher, 2020, RecSys.

Multi-Criteria Recommender Systems

Gediminas Adomavicius, YoungOk Kwon, N. Manouselis, 2011, Recommender Systems Handbook.

Impact of information feedback on bid characteristics in continuous combinatorial auctions

Shawn P. Curley, Alok Gupta, Gediminas Adomavicius, 2007 .

C-TREND: A New Technique for Identifying Trends in Transactional Data

Gediminas Adomavicius, Jesse C. Bockstedt, 2007 .

Incorporating contextual information in recommender systems using a multidimensional approach

Gediminas Adomavicius, Alexander Tuzhilin, Ramesh Sankaranarayanan, 2005, TOIS.

A Machine Learning Approach to Improving Dynamic Decision Making

Gediminas Adomavicius, Paul E. Johnson, Patrick J. O'Connor, 2014, Inf. Syst. Res..

Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data

Yingling Fan, Gediminas Adomavicius, Julian Wolfson, 2017, Sensors.

Classification, Ranking, and Top-K Stability of Recommendation Algorithms

Gediminas Adomavicius, Jingjing Zhang, Jingjing Zhang, 2016, INFORMS J. Comput..

Personalization and Recommender Systems

Gediminas Adomavicius, Zan Huang, Alexander Tuzhilin, 2008 .

Context-awareness in recommender systems: research workshop and movie recommendation challenge

Gediminas Adomavicius, Alan Said, Ernesto William De Luca, 2010, RecSys '10.

Understanding User-Generated Content and Customer Engagement on Facebook Business Pages

Gediminas Adomavicius, Mochen Yang, Yuqing Ching Ren, 2019, Inf. Syst. Res..

CEUR Workshop Proceedings

Gediminas Adomavicius, Young Ok Kwon, Gediminas Adomavicius, 2011, RecSys 2011.

RecSys'09 workshop 3: workshop on context-aware recommender systems (CARS-2009)

Francesco Ricci, Gediminas Adomavicius, 2009, RecSys '09.

Toward Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions

Alok Gupta, Gediminas Adomavicius, Alok Gupta, 2005, Inf. Syst. Res..

Scalable temporal clustering for massive multidimensional data streams

Gediminas Adomavicius, Jesse C. Bockstedt, Vishnu Parimi, 2008 .

Context-aware recommender systems

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2008, ACM Conference on Recommender Systems.

Computational feedback mechanisms for iterative multiunit multiattribute auctions

Alok Gupta, Gediminas Adomavicius, Pallab Sanyal, 2006 .

Efficient Computational Strategies for Dynamic Inventory Liquidation

Gediminas Adomavicius, Alok Gupta, Mochen Yang, 2019, Inf. Syst. Res..

Multidimensional Recommender Systems: A Data Warehousing Approach

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2001, WELCOM.

Technology roles and paths of influence in an ecosystem model of technology evolution

Robert J. Kauffman, Alok Gupta, Gediminas Adomavicius, 2007, Inf. Technol. Manag..

Personalization technologies: A process-oriented perspective

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2006, Wirtschaftsinf..

Toward Better Interactions in Recommender Systems: Cycling and Serpentining Approaches for Top-N Item Lists

Gediminas Adomavicius, Qian Zhao, F. Maxwell Harper, 2017, CSCW.

Business Computing

Gediminas Adomavicius, Alok Gupta, 2009 .

Context-aware recommender systems

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2008, RecSys '08.

Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness pdf

Gediminas Adomavicius, Annie Qu, William Li, 2020, INFORMS J. Comput..

Coalition-Based Pricing in Ascending Combinatorial Auctions

Martin Bichler, Gediminas Adomavicius, Zhen Hao, 2017, Inf. Syst. Res..

Design and Evaluation of Feedback Schemes for Multiattribute Procurement Auctions

Alok Gupta, Gediminas Adomavicius, Pallab Sanyal, 2008, ICIS.

Toward identification and adoption of best practices in algorithmic recommender systems research

Gediminas Adomavicius, Joseph A. Konstan, J. Konstan, 2013, RepSys '13.

Discovery of Actionable Patterns in Databases: the Action Hierarchy Approach

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 1997, KDD.

Recommendation Technologies: Survey of Current Methods and Possible Extensions

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2003 .

Effect of Information Feedback on Bidder Behavior in Continuous Combinatorial Auctions

Shawn P. Curley, Alok Gupta, Gediminas Adomavicius, 2012, Manag. Sci..

Understanding evolution in technology ecosystems

Robert J. Kauffman, Alok Gupta, Gediminas Adomavicius, 2008, Commun. ACM.

REQUEST: A Query Language for Customizing Recommendations

Rong Zheng, Gediminas Adomavicius, Alexander Tuzhilin, 2011, Inf. Syst. Res..

Maximizing aggregate recommendation diversity

Gediminas Adomavicius, Young Ok Kwon, Gediminas Adomavicius, 2011, RecSys 2011.

RQL: A Query Language For Recommender Systems

Rong Zheng, Gediminas Adomavicius, Alexander Tuzhilin, 2005 .

Impact of Information Feedback in Continuous Combinatorial Auctions: An Experimental Study of Economic Performance

Shawn P. Curley, Alok Gupta, Gediminas Adomavicius, 2013, MIS Q..

Designing Real-Time Feedback for Bidders in Homogeneous-Item Continuous Combinatorial Auctions

Gediminas Adomavicius, Alok Gupta, Mochen Yang, 2018, MIS Q..

Expert-Driven Validation of Rule-Based User Models in Personalization Applications

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2004, Data Mining and Knowledge Discovery.

Handling very large numbers of association rules in the analysis of microarray data

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2002, KDD.

3rd workshop on context-aware recommender systems (CARS 2011)

Francesco Ricci, Gediminas Adomavicius, Tim Hussein, 2011, RecSys '11.

User profiling in personalization applications through rule discovery and validation

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 1999, KDD '99.

Multistakeholder recommendation: Survey and research directions

Dietmar Jannach, Gediminas Adomavicius, Robin Burke, 2020, User Modeling and User-Adapted Interaction.

Integrating User Behavior and Collaborative Methods in Recommender Systems

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2004 .

Understanding longitudinal dynamics of recommender systems performance: An agent-based modeling approach

Wolfgang Ketter, Gediminas Adomavicius, Jingjing Zhang, 2013 .

Overcoming accuracy-diversity tradeoff in recommender systems

Gediminas Adomavicius, Young Ok Kwon, Gediminas Adomavicius, 2008 .

Effect of Information Feedback on the Outcomes and Dynamics of Multisourcing Multiattribute Procurement Auctions

Alok Gupta, Gediminas Adomavicius, Pallab Sanyal, 2012, J. Manag. Inf. Syst..

Making Sense of Technology Trends in the Information Technology Landscape: A Design Science Approach

Robert J. Kauffman, Alok Gupta, Gediminas Adomavicius, 2008, MIS Q..

Toward Understanding the Dynamics of Bidder Behavior in Continuous Combinatorial Auctions: Agent-Based Simulation Approach

Alok Gupta, Gediminas Adomavicius, Ali Mahdavi Adeli, 2017, HICSS.

Toward more diverse recommendations: Item re-ranking methods for recommender systems

Gediminas Adomavicius, Young Ok Kwon, YoungOk Kwon, 2009, WITS 2009.

Incorporating Context into Recommender Systems Using Multidimensional Rating Estimation Methods

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2018 .

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2005, IEEE Transactions on Knowledge and Data Engineering.

Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data

Gediminas Adomavicius, Paul E. Johnson, Patrick J. O'Connor, 2014, Data Mining and Knowledge Discovery.

Evolving decision strategies for dynamic environments: A genetic programming approach

Gediminas Adomavicius, Paul E. Johnson, Patrick J. O'Connor, 2012 .

Modeling Supply-Side Dynamics of IT Components, Products, and Infrastructure: An Empirical Analysis Using Vector Autoregression

Alok Gupta, Gediminas Adomavicius, Jesse C. Bockstedt, 2010, Inf. Syst. Res..

Validation Sequence Optimization: A Theoretical Approach

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2007, INFORMS J. Comput..

Context-Aware Recommender Systems

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2015, Recommender Systems Handbook.

C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multiattribute Transactional Data

Gediminas Adomavicius, Jesse C. Bockstedt, J. Bockstedt, 2008, IEEE Transactions on Knowledge and Data Engineering.

Maximizing Aggregate Recommendation Diversity: A Graph-Theoretic Approach

Gediminas Adomavicius, Young Ok Kwon, YoungOk Kwon, 2011, RecSys 2011.

Improving Stability of Recommender Systems: A Meta-Algorithmic Approach

Gediminas Adomavicius, Jingjing Zhang, Jingjing Zhang, 2015, IEEE Transactions on Knowledge and Data Engineering.

Personalization technologies

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2005, Commun. ACM.

Table 2, Summarized results of training and testing decision trees on synthetic data

Gediminas Adomavicius, Paul E. Johnson, Gregory W. Ramsey, 2008 .

4th workshop on context-aware recommender systems (CARS 2012)

Gediminas Adomavicius, Tim Hussein, Ernesto William De Luca, 2012, RecSys.

Context-Aware Recommender Systems

Francesco Ricci, Gediminas Adomavicius, Bamshad Mobasher, 2011, AI Mag..

De-biasing user preference ratings in recommender systems completed research paper

Shawn P. Curley, Gediminas Adomavicius, Jesse C. Bockstedt, 2014, RecSys 2014.

Integrating Behavioral, Economic, and Technical Insights to Address Algorithmic Bias: Challenges and Opportunities for IS Research

Gediminas Adomavicius, Mochen Yang, Mochen Yang, 2019, SSRN Electronic Journal.

New Recommendation Techniques for Multicriteria Rating Systems

Gediminas Adomavicius, YoungOk Kwon, Gediminas Adomavicius, 2007, IEEE Intelligent Systems.

Using Data Mining Methods to Build Customer Profiles

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2001, Computer.

Explicit or implicit feedback? engagement or satisfaction?: a field experiment on machine-learning-based recommender systems

Gediminas Adomavicius, Qian Zhao, F. Maxwell Harper, 2018, SAC.

VAMS 2017: Workshop on Value-Aware and Multistakeholder Recommendation

Yi Zhang, Gediminas Adomavicius, Ido Guy, 2017, RecSys.

Bundling Effects on Variety Seeking for Digital Information Goods

Shawn P. Curley, Gediminas Adomavicius, Jesse C. Bockstedt, 2015, J. Manag. Inf. Syst..

Recommender systems, consumer preferences, and anchoring effects

Shawn P. Curley, Gediminas Adomavicius, Jesse C. Bockstedt, 2011, RecSys 2011.

Effects of Online Recommendations on Consumers’ Willingness to Pay

Shawn P. Curley, Gediminas Adomavicius, Jesse C. Bockstedt, 2012, Decisions@RecSys.

Recommendations with a Purpose

Dietmar Jannach, Gediminas Adomavicius, D. Jannach, 2016, RecSys.

Price and Profit Awareness in Recommender Systems pdf

Dietmar Jannach, Gediminas Adomavicius, D. Jannach, 2017, ArXiv.

Multi-Criteria Recommender Systems

Gediminas Adomavicius, Nikos Manouselis, YoungOk Kwon, 2011, Recommender Systems Handbook.

From preference into decision making: modeling user interactions in recommender systems

Gediminas Adomavicius, F. Maxwell Harper, Joseph A. Konstan, 2019, RecSys.

Reducing Recommender System Biases: An Investigation of Rating Display Designs

Jingjing Zhang, Shawn P. Curley, Gediminas Adomavicius, 2019, MIS Quarterly.

Coordination and Pricing in Multi-Object Auctions

Martin Bichler, Gediminas Adomavicius, Zhen Hao, 2015, ICIS.

Iterative Smoothing Technique for Improving the Stability of Recommender Systems

Gediminas Adomavicius, Jingjing Zhang, Jingjing Zhang, 2012, RUE@RecSys.

Impact of data characteristics on recommender systems performance

Gediminas Adomavicius, Jingjing Zhang, Jingjing Zhang, 2012, TMIS.

Design and Effects of Information Feedback in Continuous Combinatorial Auctions

Shawn P. Curley, Alok Gupta, Gediminas Adomavicius, 2007, ICIS.

Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework

Jingjing Zhang, Wolfgang Ketter, Gediminas Adomavicius, 2019, Inf. Syst. Res..

Designing Intelligent Software Agents for Auctions with Limited Information Feedback

Alok Gupta, Gediminas Adomavicius, Dmitry Zhdanov, 2009, Inf. Syst. Res..

Heterogeneous Electric Vehicle Charging Coordination: A Variable Charging Speed Approach

Wolfgang Ketter, John Collins, Gediminas Adomavicius, 2019, HICSS.

Stability of Recommendation Algorithms

Gediminas Adomavicius, Jingjing Zhang, Jingjing Zhang, 2012, TOIS.

Preface to the special issue on context-aware recommender systems

Dietmar Jannach, Gediminas Adomavicius, D. Jannach, 2013, User Modeling and User-Adapted Interaction.

RecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems: Foreword

Dietmar Jannach, Gediminas Adomavicius, Robin Burke, 2011, ACM Conference on Recommender Systems.

Using Data Mining to Predict Errors in Chronic Disease Care

Gediminas Adomavicius, Paul E. Johnson, Gregory W. Ramsey, 2008 .

Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining

Gediminas Adomavicius, Gordon Burtch, Yuqing Ren, 2017, Inf. Syst. Res..

Overcoming accuracy-diversity tradeoff in recommender systems: A variance-based approach

Gediminas Adomavicius, Young Ok Kwon, YoungOk Kwon, 2008 .

[Table, Predicting Errors in Synthetic Patient Data].

Gediminas Adomavicius, Paul E. Johnson, Gregory W. Ramsey, 2008 .

Validation sequence optimization

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2007 .

Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects

Shawn P. Curley, Gediminas Adomavicius, Jesse C. Bockstedt, 2013, Inf. Syst. Res..

An Architecture of e-Butler: A Consumer-Centric Online Personalization System

Gediminas Adomavicius, Alexander Tuzhilin, A. Tuzhilin, 2002, Int. J. Comput. Intell. Appl..

Interpreting user inaction in recommender systems

Gediminas Adomavicius, Qian Zhao, F. Maxwell Harper, 2018, RecSys.

Workshop on Context-Aware Recommender Systems (CARS) 2021

Francesco Ricci, Gediminas Adomavicius, Bamshad Mobasher, 2021, RecSys.

Longitudinal Impact of Preference Biases on Recommender Systems' Performance

Gediminas Adomavicius, Jingjng Zhang, Meizi Zhou, 2021, SSRN Electronic Journal.

Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation pdf

Dietmar Jannach, Gediminas Adomavicius, Stephan Leitner, 2021, ArXiv.

Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings

Shawn P. Curley, Gediminas Adomavicius, Jesse C. Bockstedt, 2021, ACM Trans. Inf. Syst..

Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach

Gediminas Adomavicius, Yaqiong Wang, G. Adomavicius, 2021, INFORMS J. Comput..

Maximizing stability of recommendation algorithms: A collective inference approach

Gediminas Adomavicius, Jingjing Zhang, Jingjing Zhang, 2011, WITS 2011.

AN ECOSYSTEM MODEL OF TECHNOLOGY EVOLUTION

Robert J. Kauffman, Gediminas Adomavicius, Jesse C. Bockstedt, 2004 .

Impact of Recommender Systems on Consumer Preferences : A Study of Anchoring Effects

Gediminas Adomavicius, Jingjing Zhang, Jesse Bockstedt, 2009 .

A Data-Driven Exploration of Bidder Strategies in Continuous Combinatorial Auctions

Shawn P. Curley, Gediminas Adomavicius, Pallab Sanyal, 2008 .

Working Paper: Improving Recommendation Diversity Using Ranking-based Techniques Improving Recommendation Diversity Using Ranking-based Techniques

Gediminas Adomavicius, YoungOk Kwon, YoungOk Kwon, 2022 .

User acceptance of complex electronic market mechanisms: Role of information feedback

Shawn P. Curley, Gediminas Adomavicius, Pallab Sanyal, 2013 .

Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects

Gediminas Adomavicius, Jingjng Zhang, Jesse Bockstedt, 2013 .

Towards Comprehensive Bidder Support in Online Combinatorial Auctions

Gediminas Adomavicius, Alok Gupta, Alok Gupta, 2003 .

Use and Customization of Risk Scores for Predicting Cardiovascular Events Using Electronic Health Record Data

Gediminas Adomavicius, Paul E. Johnson, Julian Wolfson, 2016, Journal of the American Heart Association.