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2020 – today
- 2024
[j53]Xuan Bi
, Mochen Yang, Gediminas Adomavicius:
Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations. Inf. Syst. Res. 35(1): 339-362 (2024)- [j52]Scott Schanke, Gautam Ray, Gediminas Adomavicius, Mihir Wagle:
Content and Style of Firm-Generated Posts on Social Media: A Study of User Engagement on Hedonic and Utilitarian Product Pages on Facebook. J. Assoc. Inf. Syst. 25(2): 5 (2024) - [c32]Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, Moshe Unger:
Workshop on Context-Aware Recommender Systems (CARS) 2024. RecSys 2024: 1219-1221 - 2023
- [c31]Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, Moshe Unger:
Workshop on Context-Aware Recommender Systems 2023. RecSys 2023: 1234-1236 - 2022
- [j51]Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley, Jingjing Zhang:
Recommender Systems, Ground Truth, and Preference Pollution. AI Mag. 43(2): 177-189 (2022) - [j50]Gediminas Adomavicius, Yaqiong Wang:
Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach. INFORMS J. Comput. 34(1): 503-521 (2022) - [j49]Xuan Bi, Gediminas Adomavicius, William Li, Annie Qu:
Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness. INFORMS J. Comput. 34(3): 1644-1660 (2022) - [j48]Gediminas Adomavicius, Alok Gupta, Mochen Yang:
Bidder Support in Multi-item Multi-unit Continuous Combinatorial Auctions: A Unifying Theoretical Framework. Inf. Syst. Res. 33(4): 1174-1195 (2022) - [j47]Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley, Jingjing Zhang:
Effects of Personalized Recommendations Versus Aggregate Ratings on Post-Consumption Preference Responses. MIS Q. 46(1): 627-644 (2022) - [j46]Gediminas Adomavicius, Mochen Yang:
Integrating Behavioral, Economic, and Technical Insights to Understand and Address Algorithmic Bias: A Human-Centric Perspective. ACM Trans. Manag. Inf. Syst. 13(3): 34:1-34:27 (2022) - [c30]Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger:
CARS: Workshop on Context-Aware Recommender Systems 2022. RecSys 2022: 691-693 - [r3]Gediminas Adomavicius, Konstantin Bauman, Alexander Tuzhilin, Moshe Unger:
Context-Aware Recommender Systems: From Foundations to Recent Developments. Recommender Systems Handbook 2022: 211-250 - 2021
- [j45]Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley, Jingjing Zhang:
Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings. ACM Trans. Inf. Syst. 39(2): 13:1-13:38 (2021) - [c29]Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger:
Workshop on Context-Aware Recommender Systems (CARS) 2021. RecSys 2021: 813-814 - [i5]Gediminas Adomavicius, Dietmar Jannach, Stephan Leitner, Jingjing Zhang:
Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation. CoRR abs/2108.11068 (2021) - 2020
- [j44]Jingjing Zhang, Gediminas Adomavicius, Alok Gupta, Wolfgang Ketter:
Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework. Inf. Syst. Res. 31(1): 76-101 (2020) - [j43]Mochen Yang, Yuqing Ren, Gediminas Adomavicius:
Engagement by Design: An Empirical Study of the "Reactions" Feature on Facebook Business Pages. ACM Trans. Comput. Hum. Interact. 27(6): 43:1-43:35 (2020) - [j42]Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Augusto Pizzato:
Multistakeholder recommendation: Survey and research directions. User Model. User Adapt. Interact. 30(1): 127-158 (2020) - [c28]Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger:
Workshop on Context-Aware Recommender Systems. RecSys 2020: 635-637 - [i4]Xuan Bi, Gediminas Adomavicius, William Li, Annie Qu:
Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness. CoRR abs/2011.03452 (2020) - [i3]Mochen Yang, Edward McFowland III, Gordon Burtch, Gediminas Adomavicius:
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem. CoRR abs/2012.10790 (2020)
2010 – 2019
- 2019
- [j41]Mochen Yang, Gediminas Adomavicius, Alok Gupta:
Efficient Computational Strategies for Dynamic Inventory Liquidation. Inf. Syst. Res. 30(2): 595-615 (2019) - [j40]Mochen Yang, Yuqing Ren, Gediminas Adomavicius:
Understanding User-Generated Content and Customer Engagement on Facebook Business Pages. Inf. Syst. Res. 30(3): 839-855 (2019) - [j39]Gediminas Adomavicius, Alok Gupta, Mochen Yang:
Designing Real-Time Feedback for Bidders in Homogeneous-Item Continuous Combinatorial Auctions. MIS Q. 43(3) (2019) - [j38]Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley, Jingjing Zhang:
Reeducing Recommender System Biases: An Investigation of Rating Display Designs. MIS Q. 43(4) (2019) - [c27]Konstantina Valogianni, Wolfgang Ketter, John Collins, Gediminas Adomavicius:
Heterogeneous Electric Vehicle Charging Coordination: A Variable Charging Speed Approach. HICSS 2019: 1-10 - [c26]Qian Zhao, Martijn C. Willemsen, Gediminas Adomavicius, F. Maxwell Harper, Joseph A. Konstan:
From preference into decision making: modeling user interactions in recommender systems. RecSys 2019: 29-33 - [c25]Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger:
Workshop on context-aware recommender systems. RecSys 2019: 548-549 - [i2]Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Augusto Pizzato:
Beyond Personalization: Research Directions in Multistakeholder Recommendation. CoRR abs/1905.01986 (2019) - 2018
- [j37]Mochen Yang, Gediminas Adomavicius, Gordon Burtch, Yuqing Ren:
Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining. Inf. Syst. Res. 29(1): 4-24 (2018) - [j36]Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley, Jingjing Zhang:
Effects of Online Recommendations on Consumers' Willingness to Pay. Inf. Syst. Res. 29(1): 84-102 (2018) - [c24]Qian Zhao, Martijn C. Willemsen, Gediminas Adomavicius, F. Maxwell Harper, Joseph A. Konstan:
Interpreting user inaction in recommender systems. RecSys 2018: 40-48 - [c23]Qian Zhao, F. Maxwell Harper, Gediminas Adomavicius, Joseph A. Konstan:
Explicit or implicit feedback? engagement or satisfaction?: a field experiment on machine-learning-based recommender systems. SAC 2018: 1331-1340 - 2017
- [j35]Martin Bichler, Zhen Hao, Gediminas Adomavicius:
Coalition-Based Pricing in Ascending Combinatorial Auctions. Inf. Syst. Res. 28(1): 159-179 (2017) - [j34]Bryan D. Martin, Vittorio Addona, Julian Wolfson, Gediminas Adomavicius, Yingling Fan:
Methods for Real-Time Prediction of the Mode of Travel Using Smartphone-Based GPS and Accelerometer Data. Sensors 17(9): 2058 (2017) - [c22]Qian Zhao, Gediminas Adomavicius, F. Maxwell Harper, Martijn C. Willemsen, Joseph A. Konstan:
Toward Better Interactions in Recommender Systems: Cycling and Serpentining Approaches for Top-N Item Lists. CSCW 2017: 1444-1453 - [c21]Ali Mahdavi Adeli, Gediminas Adomavicius, Alok Gupta:
Toward Understanding the Dynamics of Bidder Behavior in Continuous Combinatorial Auctions: Agent-Based Simulation Approach. HICSS 2017: 1-10 - [c20]Robin Burke, Gediminas Adomavicius, Ido Guy, Jan Krasnodebski, Luiz Augusto Pizzato, Yi Zhang, Himan Abdollahpouri:
VAMS 2017: Workshop on Value-Aware and Multistakeholder Recommendation. RecSys 2017: 378-379 - [i1]Dietmar Jannach, Gediminas Adomavicius:
Price and Profit Awareness in Recommender Systems. CoRR abs/1707.08029 (2017) - 2016
- [j33]Gediminas Adomavicius, Jingjing Zhang:
Classification, Ranking, and Top-K Stability of Recommendation Algorithms. INFORMS J. Comput. 28(1): 129-147 (2016) - [j32]David M. Vock, Julian Wolfson, Sunayan Bandyopadhyay, Gediminas Adomavicius, Paul E. Johnson, Gabriela Vazquez-Benitez, Patrick J. O'Connor:
Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting. J. Biomed. Informatics 61: 119-131 (2016) - [c19]Dietmar Jannach, Gediminas Adomavicius:
Recommendations with a Purpose. RecSys 2016: 7-10 - [c18]Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley, Jingjing Zhang:
Understanding Effects of Personalized vs. Aggregate Ratings on User Preferences. IntRS@RecSys 2016: 14-21 - 2015
- [j31]Sunayan Bandyopadhyay, Julian Wolfson, David M. Vock, Gabriela Vazquez-Benitez, Gediminas Adomavicius, Mohamed Elidrisi, Paul E. Johnson, Patrick J. O'Connor:
Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data. Data Min. Knowl. Discov. 29(4): 1033-1069 (2015) - [j30]Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley:
Bundling Effects on Variety Seeking for Digital Information Goods. J. Manag. Inf. Syst. 31(4): 182-212 (2015) - [j29]Gediminas Adomavicius, Jingjing Zhang:
Improving Stability of Recommender Systems: A Meta-Algorithmic Approach. IEEE Trans. Knowl. Data Eng. 27(6): 1573-1587 (2015) - [c17]Martin Bichler, Zhen Hao, Gediminas Adomavicius:
Coordination and Pricing in Multi-Object Auctions. ICIS 2015 - [r2]Gediminas Adomavicius, Alexander Tuzhilin:
Context-Aware Recommender Systems. Recommender Systems Handbook 2015: 191-226 - [r1]Gediminas Adomavicius, YoungOk Kwon:
Multi-Criteria Recommender Systems. Recommender Systems Handbook 2015: 847-880 - 2014
- [j28]Gediminas Adomavicius, YoungOk Kwon:
Optimization-Based Approaches for Maximizing Aggregate Recommendation Diversity. INFORMS J. Comput. 26(2): 351-369 (2014) - [j27]Georg Meyer, Gediminas Adomavicius, Paul E. Johnson, Mohamed Elidrisi, William A. Rush, JoAnn M. Sperl-Hillen, Patrick J. O'Connor:
A Machine Learning Approach to Improving Dynamic Decision Making. Inf. Syst. Res. 25(2): 239-263 (2014) - [j26]Gediminas Adomavicius, Dietmar Jannach:
Preface to the special issue on context-aware recommender systems. User Model. User Adapt. Interact. 24(1-2): 1-5 (2014) - 2013
- [j25]Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley, Jingjing Zhang:
Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects. Inf. Syst. Res. 24(4): 956-975 (2013) - [j24]Gediminas Adomavicius, Shawn P. Curley, Alok Gupta, Pallab Sanyal:
Impact of Information Feedback in Continuous Combinatorial Auctions: An Experimental Study of Economic Performance. MIS Q. 37(1): 55-76 (2013) - [c16]Joseph A. Konstan, Gediminas Adomavicius:
Toward identification and adoption of best practices in algorithmic recommender systems research. RepSys@RecSys 2013: 23-28 - 2012
- [j23]Gediminas Adomavicius, Jesse C. Bockstedt, Alok Gupta:
Modeling Supply-Side Dynamics of IT Components, Products, and Infrastructure: An Empirical Analysis Using Vector Autoregression. Inf. Syst. Res. 23(2): 397-417 (2012) - [j22]Gediminas Adomavicius, Alok Gupta, Pallab Sanyal:
Effect of Information Feedback on the Outcomes and Dynamics of Multisourcing Multiattribute Procurement Auctions. J. Manag. Inf. Syst. 28(4): 199-230 (2012) - [j21]Gediminas Adomavicius, Shawn P. Curley, Alok Gupta, Pallab Sanyal:
Effect of Information Feedback on Bidder Behavior in Continuous Combinatorial Auctions. Manag. Sci. 58(4): 811-830 (2012) - [j20]Gediminas Adomavicius, YoungOk Kwon:
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques. IEEE Trans. Knowl. Data Eng. 24(5): 896-911 (2012) - [j19]Gediminas Adomavicius, Jingjing Zhang:
Impact of data characteristics on recommender systems performance. ACM Trans. Manag. Inf. Syst. 3(1): 3:1-3:17 (2012) - [j18]Gediminas Adomavicius, Jingjing Zhang:
Stability of Recommendation Algorithms. ACM Trans. Inf. Syst. 30(4): 23:1-23:31 (2012) - [c15]Gediminas Adomavicius, Jingjing Zhang:
Iterative Smoothing Technique for Improving the Stability of Recommender Systems. RUE@RecSys 2012: 3-8 - [c14]Gediminas Adomavicius, Jesse C. Bockstedt, Shawn P. Curley, Jingjing Zhang:
Effects of Online Recommendations on Consumers' Willingness to Pay. Decisions@RecSys 2012: 40-45 - [c13]Gediminas Adomavicius, Linas Baltrunas, Ernesto William De Luca, Tim Hussein, Alexander Tuzhilin:
4th workshop on context-aware recommender systems (CARS 2012). RecSys 2012: 349-350 - 2011
- [j17]Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin:
Context-Aware Recommender Systems. AI Mag. 32(3): 67-80 (2011) - [j16]Gediminas Adomavicius, Alexander Tuzhilin, Rong Zheng:
REQUEST: A Query Language for Customizing Recommendations. Inf. Syst. Res. 22(1): 99-117 (2011) - [c12]Gediminas Adomavicius, Linas Baltrunas, Tim Hussein, Francesco Ricci, Alexander Tuzhilin:
3rd workshop on context-aware recommender systems (CARS 2011). RecSys 2011: 379-380 - [p2]Gediminas Adomavicius, Alexander Tuzhilin:
Context-Aware Recommender Systems. Recommender Systems Handbook 2011: 217-253 - [p1]Gediminas Adomavicius, Nikos Manouselis, YoungOk Kwon:
Multi-Criteria Recommender Systems. Recommender Systems Handbook 2011: 769-803 - [e1]Bamshad Mobasher, Robin D. Burke, Dietmar Jannach, Gediminas Adomavicius:
Proceedings of the 2011 ACM Conference on Recommender Systems, RecSys 2011, Chicago, IL, USA, October 23-27, 2011. ACM 2011, ISBN 978-1-4503-0683-6 [contents] - 2010
- [c11]Gediminas Adomavicius, Jingjing Zhang:
On the stability of recommendation algorithms. RecSys 2010: 47-54 - [c10]Gediminas Adomavicius, Alexander Tuzhilin, Shlomo Berkovsky, Ernesto William De Luca, Alan Said:
Context-awareness in recommender systems: research workshop and movie recommendation challenge. RecSys 2010: 385-386
2000 – 2009
- 2009
- [j15]Gediminas Adomavicius, Alok Gupta, Dmitry Zhdanov:
Designing Intelligent Software Agents for Auctions with Limited Information Feedback. Inf. Syst. Res. 20(4): 507-526 (2009) - [c9]Gediminas Adomavicius, Francesco Ricci:
RecSys'09 workshop 3: workshop on context-aware recommender systems (CARS-2009). RecSys 2009: 423-424 - 2008
- [j14]Gediminas Adomavicius, Jesse C. Bockstedt, Alok Gupta, Robert J. Kauffman:
Understanding evolution in technology ecosystems. Commun. ACM 51(10): 117-122 (2008) - [j13]Gediminas Adomavicius, Jesse C. Bockstedt, Alok Gupta, Robert J. Kauffman:
Making Sense of Technology Trends in the Information Technology Landscape: A Design Science Approach. MIS Q. 32(4): 779-809 (2008) - [j12]Gediminas Adomavicius, Jesse C. Bockstedt:
C-TREND: Temporal Cluster Graphs for Identifying and Visualizing Trends in Multiattribute Transactional Data. IEEE Trans. Knowl. Data Eng. 20(6): 721-735 (2008) - [c8]Gediminas Adomavicius, Alok Gupta, Pallab Sanyal:
Design and Evaluation of Feedback Schemes for Multiattribute Procurement Auctions. ICIS 2008: 32 - [c7]Gediminas Adomavicius, Alexander Tuzhilin:
Context-aware recommender systems. RecSys 2008: 335-336 - 2007
- [j11]Gediminas Adomavicius, YoungOk Kwon:
New Recommendation Techniques for Multicriteria Rating Systems. IEEE Intell. Syst. 22(3): 48-55 (2007) - [j10]Gediminas Adomavicius, Alexander Tuzhilin:
Validation Sequence Optimization: A Theoretical Approach. INFORMS J. Comput. 19(2): 185-200 (2007) - [j9]Gediminas Adomavicius, Jesse C. Bockstedt, Alok Gupta, Robert J. Kauffman:
Technology roles and paths of influence in an ecosystem model of technology evolution. Inf. Technol. Manag. 8(2): 185-202 (2007) - [c6]Gediminas Adomavicius, Pallab Sanyal, Alok Gupta, Shawn P. Curley:
Design and Effects of Information Feedback in Continuous Combinatorial Auctions. ICIS 2007: 107 - 2006
- [j8]Gediminas Adomavicius, Alexander Tuzhilin:
Personalization technologies: A process-oriented perspective. Wirtschaftsinf. 48(6): 449-450 (2006) - [c5]Gediminas Adomavicius, Jesse C. Bockstedt, Alok Gupta, Robert J. Kauffman:
Understanding Patterns of Technology Evolution: An Ecosystem Perspective. HICSS 2006 - 2005
- [j7]Gediminas Adomavicius, Alexander Tuzhilin:
Personalization technologies: a process-oriented perspective. Commun. ACM 48(10): 83-90 (2005) - [j6]Gediminas Adomavicius, Alok Gupta:
Toward Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions. Inf. Syst. Res. 16(2): 169-185 (2005) - [j5]Gediminas Adomavicius, Alexander Tuzhilin:
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. Knowl. Data Eng. 17(6): 734-749 (2005) - [j4]Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, Alexander Tuzhilin:
Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. 23(1): 103-145 (2005) - 2002
- [b1]Gediminas Adomavicius:
Expert-Driven Validation of Set-Based Data Mining Results. New York University, USA, 2002 - [j3]Gediminas Adomavicius, Alexander Tuzhilin:
An Architecture of e-Butler: A Consumer-Centric Online Personalization System. Int. J. Comput. Intell. Appl. 2(3): 313-327 (2002) - [c4]Alexander Tuzhilin, Gediminas Adomavicius:
Handling very large numbers of association rules in the analysis of microarray data. KDD 2002: 396-404 - 2001
- [j2]Gediminas Adomavicius, Alexander Tuzhilin:
Using Data Mining Methods to Build Customer Profiles. Computer 34(2): 74-82 (2001) - [j1]Gediminas Adomavicius, Alexander Tuzhilin:
Expert-Driven Validation of Rule-Based User Models in Personalization Applications. Data Min. Knowl. Discov. 5(1/2): 33-58 (2001) - [c3]Gediminas Adomavicius, Alexander Tuzhilin:
Multidimensional Recommender Systems: A Data Warehousing Approach. WELCOM 2001: 180-192
1990 – 1999
- 1999
- [c2]Gediminas Adomavicius, Alexander Tuzhilin:
User Profiling in Personalization Applications Through Rule Discovery and Validation. KDD 1999: 377-381 - 1997
- [c1]Gediminas Adomavicius, Alexander Tuzhilin:
Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach. KDD 1997: 111-114
Coauthor Index
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