Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach. - Gediminas Adomavicius; Alexander Tuzhilin (KDD, 1997)

User Profiling in Personalization Applications Through Rule Discovery and Validation. - Gediminas Adomavicius; Alexander Tuzhilin (KDD, 1999)

Multidimensional Recommender Systems: A Data Warehousing Approach. - Gediminas Adomavicius; Alexander Tuzhilin (WELCOM, 2001)

Using Data Mining Methods to Build Customer Profiles. - Gediminas Adomavicius; Alexander Tuzhilin (Computer, 2001)

Expert-Driven Validation of Rule-Based User Models in Personalization Applications. - Gediminas Adomavicius; Alexander Tuzhilin (Data Min. Knowl. Discov., 2001)

Handling very large numbers of association rules in the analysis of microarray data. - Alexander Tuzhilin; Gediminas Adomavicius (KDD, 2002)

An Architecture of e-Butler: A Consumer-Centric Online Personalization System. - Gediminas Adomavicius; Alexander Tuzhilin (Int. J. Comput. Intell. Appl., 2002)

Expert-Driven Validation of Set-Based Data Mining Results. - Gediminas Adomavicius (2002)

Personalization technologies: a process-oriented perspective. - Gediminas Adomavicius; Alexander Tuzhilin (Commun. ACM, 2005)

Toward Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions. - Gediminas Adomavicius; Alok Gupta (Inf. Syst. Res., 2005)

Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. - Gediminas Adomavicius; Alexander Tuzhilin (IEEE Trans. Knowl. Data Eng., 2005)

Incorporating contextual information in recommender systems using a multidimensional approach. - Gediminas Adomavicius; Ramesh Sankaranarayanan; Shahana Sen; Alexander Tuzhilin (ACM Trans. Inf. Syst., 2005)

Understanding Patterns of Technology Evolution: An Ecosystem Perspective. - Gediminas Adomavicius; Jesse C. Bockstedt; Alok Gupta; Robert J. Kauffman (HICSS, 2006)

Design and Effects of Information Feedback in Continuous Combinatorial Auctions. - Gediminas Adomavicius; Pallab Sanyal; Alok Gupta; Shawn P. Curley (ICIS, 2007)

New Recommendation Techniques for Multicriteria Rating Systems. - Gediminas Adomavicius; YoungOk Kwon (IEEE Intell. Syst., 2007)

Validation Sequence Optimization: A Theoretical Approach. - Gediminas Adomavicius; Alexander Tuzhilin (INFORMS J. Comput., 2007)

Technology roles and paths of influence in an ecosystem model of technology evolution. - Gediminas Adomavicius; Jesse C. Bockstedt; Alok Gupta; Robert J. Kauffman (Inf. Technol. Manag., 2007)

Design and Evaluation of Feedback Schemes for Multiattribute Procurement Auctions. - Gediminas Adomavicius; Alok Gupta; Pallab Sanyal (ICIS, 2008)

Context-aware recommender systems. - Gediminas Adomavicius; Alexander Tuzhilin (RecSys, 2008)

Understanding evolution in technology ecosystems. - Gediminas Adomavicius; Jesse C. Bockstedt; Alok Gupta; Robert J. Kauffman (Commun. ACM, 2008)

Making Sense of Technology Trends in the Information Technology Landscape: A Design Science Approach. - Gediminas Adomavicius; Jesse C. Bockstedt; Alok Gupta; Robert J. Kauffman (MIS Q., 2008)

C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multiattribute Transactional Data. - Gediminas Adomavicius; Jesse C. Bockstedt (IEEE Trans. Knowl. Data Eng., 2008)

RecSys'09 workshop 3: workshop on context-aware recommender systems (CARS-2009). - Gediminas Adomavicius; Francesco Ricci 001 (RecSys, 2009)

Designing Intelligent Software Agents for Auctions with Limited Information Feedback. - Gediminas Adomavicius; Alok Gupta; Dmitry Zhdanov (Inf. Syst. Res., 2009)

Context-awareness in recommender systems: research workshop and movie recommendation challenge. - Gediminas Adomavicius; Alexander Tuzhilin; Shlomo Berkovsky; Ernesto William De Luca; Alan Said (RecSys, 2010)

3rd workshop on context-aware recommender systems (CARS 2011). - Gediminas Adomavicius; Linas Baltrunas; Tim Hussein; Francesco Ricci 001; Alexander Tuzhilin (RecSys, 2011)

Context-Aware Recommender Systems. - Gediminas Adomavicius; Bamshad Mobasher; Francesco Ricci 001; Alexander Tuzhilin (AI Mag., 2011)

REQUEST: A Query Language for Customizing Recommendations. - Gediminas Adomavicius; Alexander Tuzhilin; Rong Zheng 003 (Inf. Syst. Res., 2011)

Multi-Criteria Recommender Systems. - Gediminas Adomavicius; Nikos Manouselis; YoungOk Kwon (Recommender Systems Handbook, 2011)

Context-Aware Recommender Systems. - Gediminas Adomavicius; Alexander Tuzhilin (Recommender Systems Handbook, 2011)

4th workshop on context-aware recommender systems (CARS 2012). - Gediminas Adomavicius; Linas Baltrunas; Ernesto William De Luca; Tim Hussein; Alexander Tuzhilin (RecSys, 2012)

Iterative Smoothing Technique for Improving the Stability of Recommender Systems. - Gediminas Adomavicius; Jingjing Zhang 001 (RUE@RecSys, 2012)

Modeling Supply-Side Dynamics of IT Components, Products, and Infrastructure: An Empirical Analysis Using Vector Autoregression. - Gediminas Adomavicius; Jesse C. Bockstedt; Alok Gupta (Inf. Syst. Res., 2012)

Effect of Information Feedback on the Outcomes and Dynamics of Multisourcing Multiattribute Procurement Auctions. - Gediminas Adomavicius; Alok Gupta; Pallab Sanyal (J. Manag. Inf. Syst., 2012)

Effect of Information Feedback on Bidder Behavior in Continuous Combinatorial Auctions. - Gediminas Adomavicius; Shawn P. Curley; Alok Gupta; Pallab Sanyal (Manag. Sci., 2012)

Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques. - Gediminas Adomavicius; YoungOk Kwon (IEEE Trans. Knowl. Data Eng., 2012)

Impact of data characteristics on recommender systems performance. - Gediminas Adomavicius; Jingjing Zhang 001 (ACM Trans. Manag. Inf. Syst., 2012)

Stability of Recommendation Algorithms. - Gediminas Adomavicius; Jingjing Zhang 001 (ACM Trans. Inf. Syst., 2012)

Toward identification and adoption of best practices in algorithmic recommender systems research. - Joseph A. Konstan; Gediminas Adomavicius (RepSys@RecSys, 2013)

Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects. - Gediminas Adomavicius; Jesse C. Bockstedt; Shawn P. Curley; Jingjing Zhang 001 (Inf. Syst. Res., 2013)

Impact of Information Feedback in Continuous Combinatorial Auctions: An Experimental Study of Economic Performance. - Gediminas Adomavicius; Shawn P. Curley; Alok Gupta; Pallab Sanyal (MIS Q., 2013)

Optimization-Based Approaches for Maximizing Aggregate Recommendation Diversity. - Gediminas Adomavicius; YoungOk Kwon (INFORMS J. Comput., 2014)

A Machine Learning Approach to Improving Dynamic Decision Making. - Georg Meyer; Gediminas Adomavicius; Paul E. Johnson; Mohamed Elidrisi; William A. Rush; JoAnn M. Sperl-Hillen; Patrick J. O'Connor (Inf. Syst. Res., 2014)

Preface to the special issue on context-aware recommender systems. - Gediminas Adomavicius; Dietmar Jannach (User Model. User Adapt. Interact., 2014)

Coordination and Pricing in Multi-Object Auctions. - Martin Bichler; Zhen Hao; Gediminas Adomavicius (ICIS, 2015)

Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data. - Sunayan Bandyopadhyay; Julian Wolfson; David M. Vock; Gabriela Vazquez-Benitez; Gediminas Adomavicius; Mohamed Elidrisi; Paul E. Johnson; Patrick J. O'Connor (Data Min. Knowl. Discov., 2015)

Bundling Effects on Variety Seeking for Digital Information Goods. - Gediminas Adomavicius; Jesse C. Bockstedt; Shawn P. Curley (J. Manag. Inf. Syst., 2015)

Improving Stability of Recommender Systems: A Meta-Algorithmic Approach. - Gediminas Adomavicius; Jingjing Zhang 001 (IEEE Trans. Knowl. Data Eng., 2015)

Multi-Criteria Recommender Systems. - Gediminas Adomavicius; YoungOk Kwon (Recommender Systems Handbook, 2015)

Context-Aware Recommender Systems. - Gediminas Adomavicius; Alexander Tuzhilin (Recommender Systems Handbook, 2015)

Understanding Effects of Personalized vs. Aggregate Ratings on User Preferences. - Gediminas Adomavicius; Jesse C. Bockstedt; Shawn P. Curley; Jingjing Zhang 001 (IntRS@RecSys, 2016)

Recommendations with a Purpose. - Dietmar Jannach; Gediminas Adomavicius (RecSys, 2016)

Classification, Ranking, and Top-K Stability of Recommendation Algorithms. - Gediminas Adomavicius; Jingjing Zhang 001 (INFORMS J. Comput., 2016)

Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting. - David M. Vock; Julian Wolfson; Sunayan Bandyopadhyay; Gediminas Adomavicius; Paul E. Johnson; Gabriela Vazquez-Benitez; Patrick J. O'Connor (J. Biomed. Informatics, 2016)

Toward Better Interactions in Recommender Systems: Cycling and Serpentining Approaches for Top-N Item Lists. - Qian Zhao; Gediminas Adomavicius; F. Maxwell Harper; Martijn C. Willemsen; Joseph A. Konstan (CSCW, 2017)

Toward Understanding the Dynamics of Bidder Behavior in Continuous Combinatorial Auctions: Agent-Based Simulation Approach. - Ali Mahdavi Adeli; Gediminas Adomavicius; Alok Gupta (HICSS, 2017)

VAMS 2017: Workshop on Value-Aware and Multistakeholder Recommendation. - Robin Burke; Gediminas Adomavicius; Ido Guy; Jan Krasnodebski; Luiz Augusto Pizzato; Yi Zhang 001; Himan Abdollahpouri (RecSys, 2017)

Price and Profit Awareness in Recommender Systems. - Dietmar Jannach; Gediminas Adomavicius (CoRR, 2017)

Coalition-Based Pricing in Ascending Combinatorial Auctions. - Martin Bichler; Zhen Hao; Gediminas Adomavicius (Inf. Syst. Res., 2017)

Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data. - Bryan D. Martin; Vittorio Addona; Julian Wolfson; Gediminas Adomavicius; Yingling Fan (Sensors, 2017)

Interpreting user inaction in recommender systems. - Qian Zhao; Martijn C. Willemsen; Gediminas Adomavicius; F. Maxwell Harper; Joseph A. Konstan (RecSys, 2018)

Explicit or implicit feedback? engagement or satisfaction?: a field experiment on machine-learning-based recommender systems. - Qian Zhao; F. Maxwell Harper; Gediminas Adomavicius; Joseph A. Konstan (SAC, 2018)

Effects of Online Recommendations on Consumers' Willingness to Pay. - Gediminas Adomavicius; Jesse C. Bockstedt; Shawn P. Curley; Jingjing Zhang 001 (Inf. Syst. Res., 2018)

Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining. - Mochen Yang; Gediminas Adomavicius; Gordon Burtch; Yuqing Ren (Inf. Syst. Res., 2018)

Heterogeneous Electric Vehicle Charging Coordination: A Variable Charging Speed Approach. - Konstantina Valogianni; Wolfgang Ketter; John Collins; Gediminas Adomavicius (HICSS, 2019)

From preference into decision making: modeling user interactions in recommender systems. - Qian Zhao; Martijn C. Willemsen; Gediminas Adomavicius; F. Maxwell Harper; Joseph A. Konstan (RecSys, 2019)

Beyond Personalization: Research Directions in Multistakeholder Recommendation. - Himan Abdollahpouri; Gediminas Adomavicius; Robin Burke; Ido Guy; Dietmar Jannach; Toshihiro Kamishima; Jan Krasnodebski; Luiz Augusto Pizzato (CoRR, 2019)

Efficient Computational Strategies for Dynamic Inventory Liquidation. - Mochen Yang; Gediminas Adomavicius; Alok Gupta (Inf. Syst. Res., 2019)

Understanding User-Generated Content and Customer Engagement on Facebook Business Pages. - Mochen Yang; Yuqing Ren; Gediminas Adomavicius (Inf. Syst. Res., 2019)

Reeducing Recommender System Biases: An Investigation of Rating Display Designs. - Gediminas Adomavicius; Jesse C. Bockstedt; Shawn P. Curley; Jingjing Zhang 001 (MIS Q., 2019)

Designing Real-Time Feedback for Bidders in Homogeneous-Item Continuous Combinatorial Auctions. - Gediminas Adomavicius; Alok Gupta; Mochen Yang (MIS Q., 2019)

Workshop on Context-Aware Recommender Systems. - Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Francesco Ricci 001; Alexander Tuzhilin; Moshe Unger (RecSys, 2020)

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem. - Mochen Yang; Edward McFowland III; Gordon Burtch; Gediminas Adomavicius (CoRR, 2020)

Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework. - Jingjing Zhang 001; Gediminas Adomavicius; Alok Gupta; Wolfgang Ketter (Inf. Syst. Res., 2020)

Engagement by Design: An Empirical Study of the "Reactions" Feature on Facebook Business Pages. - Mochen Yang; Yuqing Ren; Gediminas Adomavicius (ACM Trans. Comput. Hum. Interact., 2020)

Multistakeholder recommendation: Survey and research directions. - Himan Abdollahpouri; Gediminas Adomavicius; Robin Burke; Ido Guy; Dietmar Jannach; Toshihiro Kamishima; Jan Krasnodebski; Luiz Augusto Pizzato (User Model. User Adapt. Interact., 2020)

Workshop on Context-Aware Recommender Systems (CARS) 2021. - Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Francesco Ricci 001; Alexander Tuzhilin; Moshe Unger (RecSys, 2021)

Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation. - Gediminas Adomavicius; Dietmar Jannach; Stephan Leitner; Jingjing Zhang 001 (CoRR, 2021)

Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings. - Gediminas Adomavicius; Jesse C. Bockstedt; Shawn P. Curley; Jingjing Zhang 001 (ACM Trans. Inf. Syst., 2021)

CARS: Workshop on Context-Aware Recommender Systems 2022. - Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Francesco Ricci 001; Alexander Tuzhilin; Moshe Unger (RecSys, 2022)

Recommender Systems, Ground Truth, and Preference Pollution. - Gediminas Adomavicius; Jesse C. Bockstedt; Shawn P. Curley; Jingjing Zhang 001 (AI Mag., 2022)

Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach. - Gediminas Adomavicius; Yaqiong Wang (INFORMS J. Comput., 2022)

Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness. - Xuan Bi; Gediminas Adomavicius; William Li; Annie Qu (INFORMS J. Comput., 2022)

Effects of Personalized Recommendations Versus Aggregate Ratings on Post-Consumption Preference Responses. - Gediminas Adomavicius; Jesse C. Bockstedt; Shawn P. Curley; Jingjing Zhang 001 (MIS Q., 2022)

Integrating Behavioral, Economic, and Technical Insights to Understand and Address Algorithmic Bias: A Human-Centric Perspective. - Gediminas Adomavicius; Mochen Yang (ACM Trans. Manag. Inf. Syst., 2022)

Context-Aware Recommender Systems: From Foundations to Recent Developments. - Gediminas Adomavicius; Konstantin Bauman; Alexander Tuzhilin; Moshe Unger (Recommender Systems Handbook, 2022)

Bidder Support in Multi-item Multi-unit Continuous Combinatorial Auctions: A Unifying Theoretical Framework. - Gediminas Adomavicius; Alok Gupta; Mochen Yang (Inf. Syst. Res., 2022)

Workshop on Context-Aware Recommender Systems 2023. - Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Alexander Tuzhilin; Moshe Unger (RecSys, 2023)

Workshop on Context-Aware Recommender Systems (CARS) 2024. - Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Alexander Tuzhilin; Moshe Unger (RecSys, 2024)

Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations. - Xuan Bi; Mochen Yang; Gediminas Adomavicius (Inf. Syst. Res., 2024)

Longitudinal Impact of Preference Biases on Recommender Systems' Performance. - Meizi Zhou; Jingjing Zhang 001; Gediminas Adomavicius (Inf. Syst. Res., 2024)

Content and Style of Firm-Generated Posts on Social Media: A Study of User Engagement on Hedonic and Utilitarian Product Pages on Facebook. - Scott Schanke; Gautam Ray; Gediminas Adomavicius; Mihir Wagle (J. Assoc. Inf. Syst., 2024)

De-centering the (Traditional) user: Multistakeholder evaluation of recommender systems. - Robin Burke; Gediminas Adomavicius; Toine Bogers; Tommaso Di Noia; Dominik Kowald; Julia Neidhardt; Özlem Özgöbek; Maria Soledad Pera; Nava Tintarev; Jürgen Ziegler 001 (Int. J. Hum. Comput. Stud., 2025)

Efficient and Flexible Long-Tail Recommendation Using Cosine Patterns. - Yaqiong Wang; Junjie Wu 002; Zhiang Wu 001; Gediminas Adomavicius (INFORMS J. Comput., 2025)