About

Gedas Adomavicius is a Professor in Business Analytics and Information Systems at the Carlson School of Management. He holds the Curtis L. Carlson Chair and serves as the Academic Director of the Carlson Analytics Lab. His work is closely affiliated with the Carlson School's MS in Business Analytics program and the Carlson Analytics Lab, where graduate students study a broad range of data analysis techniques and apply them to real business problems. These students are skilled in exploratory data visualization, predictive analytics, programming, data engineering, machine learning methods, and more, emerging as data science professionals. Partner organizations have the opportunity to collaborate with these talented students while supporting the educational mission of the programs.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Business
• Economics
• Information Retrieval
• Mathematics
• Political Science
• World Wide Web
• Data science
• Knowledge management
• Marketing
• Operations research
• Statistics
• Internet privacy
• Econometrics
• Risk analysis (engineering)
• Management science
• Psychology
• Advertising

Selected publications

• Recommending Composite Items Using Multi-Level Preference Information: A Joint Interaction Modeling Approach

  ArXiv.org · 2026-01-26

  articleOpen access

  With the advancement of machine learning and artificial intelligence technologies, recommender systems have been increasingly used across a vast variety of platforms to efficiently and effectively match users with items. As application contexts become more diverse and complex, there is a growing need for more sophisticated recommendation techniques. One example is the composite item (for example, fashion outfit) recommendation where multiple levels of user preference information might be available and relevant. In this study, we propose JIMA, a joint interaction modeling approach that uses a single model to take advantage of all data from different levels of granularity and incorporate interactions to learn the complex relationships among lower-order (atomic item) and higher-order (composite item) user preferences as well as domain expertise (e.g., on the stylistic fit). We comprehensively evaluate the proposed method and compare it with advanced baselines through multiple simulation studies as well as with real data in both offline and online settings. The results consistently demonstrate the superior performance of the proposed approach.

  PublisherOA PDF

• Recommending Composite Items Using Multi-Level Preference Information: A Joint Interaction Modeling Approach

  Open MIND · 2026-01-26

  preprint

  With the advancement of machine learning and artificial intelligence technologies, recommender systems have been increasingly used across a vast variety of platforms to efficiently and effectively match users with items. As application contexts become more diverse and complex, there is a growing need for more sophisticated recommendation techniques. One example is the composite item (for example, fashion outfit) recommendation where multiple levels of user preference information might be available and relevant. In this study, we propose JIMA, a joint interaction modeling approach that uses a single model to take advantage of all data from different levels of granularity and incorporate interactions to learn the complex relationships among lower-order (atomic item) and higher-order (composite item) user preferences as well as domain expertise (e.g., on the stylistic fit). We comprehensively evaluate the proposed method and compare it with advanced baselines through multiple simulation studies as well as with real data in both offline and online settings. The results consistently demonstrate the superior performance of the proposed approach.

  DOI

• Workshop on Context-Aware Recommender Systems

  2025-09-06

  article1st authorCorresponding
  PublisherDOI

• Toward Sustainable Electricity Markets: Capacity-Based Pricing for Electric Vehicle Smart Charging

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Robustness is Important: Limitations of LLMs for Data Fitting

  ArXiv.org · 2025-08-27

  preprintOpen accessSenior author

  Large Language Models (LLMs) are being applied in a wide array of settings, well beyond the typical language-oriented use cases. In particular, LLMs are increasingly used as a plug-and-play method for fitting data and generating predictions. Prior work has shown that LLMs, via in-context learning or supervised fine-tuning, can perform competitively with many tabular supervised learning techniques in terms of predictive performance. However, we identify a critical vulnerability of using LLMs for data fitting -- making changes to data representation that are completely irrelevant to the underlying learning task can drastically alter LLMs' predictions on the same data. For example, simply changing variable names can sway the size of prediction error by as much as 82% in certain settings. Such prediction sensitivity with respect to task-irrelevant variations manifests under both in-context learning and supervised fine-tuning, for both close-weight and open-weight general-purpose LLMs. Moreover, by examining the attention scores of an open-weight LLM, we discover a non-uniform attention pattern: training examples and variable names/values which happen to occupy certain positions in the prompt receive more attention when output tokens are generated, even though different positions are expected to receive roughly the same attention. This partially explains the sensitivity in the presence of task-irrelevant variations. We also consider a state-of-the-art tabular foundation model (TabPFN) trained specifically for data fitting. Despite being explicitly designed to achieve prediction robustness, TabPFN is still not immune to task-irrelevant variations. Overall, despite LLMs' impressive predictive capabilities, currently they lack even the basic level of robustness to be used as a principled data-fitting tool.

  PublisherOA PDFDOI

• De-centering the (Traditional) User: Multistakeholder Evaluation of Recommender Systems

  arXiv (Cornell University) · 2025-01-09 · 3 citations

  preprintOpen access

  Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due to their complexity, these systems cannot be evaluated strictly by the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this article, we focus our discussion on the challenges of multistakeholder evaluation of recommender systems. We bring attention to the different aspects involved -- from the range of stakeholders involved (including but not limited to providers and consumers) to the values and specific goals of each relevant stakeholder. We discuss how to move from theoretical principles to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. We aim to provide guidance to researchers and practitioners about incorporating these complex and domain-dependent issues of evaluation in the course of designing, developing, and researching applications with multistakeholder aspects.

  PublisherOA PDFDOI

• De-centering the (Traditional) user: Multistakeholder evaluation of recommender systems

  International Journal of Human-Computer Studies · 2025-07-17 · 8 citations

  articleOpen access
  PublisherOA PDFDOI

• Toward Sustainable Electricity Markets: Capacity-Based Pricing for Electric Vehicle Smart Charging

  Information Systems Research · 2025-06-24 · 3 citations

  articleSenior author

  As electric vehicles (EVs) become more widespread, cities face the growing challenge of managing charging demand without overloading the grid. This study presents a novel information systems (IS) solution that supports smart and sustainable EV integration. The authors develop a capacity-based pricing model that adjusts in real time based on charging rates and grid capacity. Unlike many existing approaches, it avoids “avalanche effects” where synchronized charging behavior creates new demand peaks. The presented solution is also computationally efficient, making it practical for real-world use. Evaluated through simulations based on realistic urban scenarios, the model reduces demand volatility, aligns EV charging with renewable energy availability, and maintains overall charging costs for users. This work offers policy makers and energy providers a concrete tool to balance environmental goals with energy system reliability. For urban mobility planners, it provides a scalable, adaptive method to support the transition to cleaner urban mobility.

  PublisherDOI

• Improving Convergence of Flexible Combinatorial Auctions with Rationality-Based Ask Prices

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Workshop on Context-Aware Recommender Systems (CARS) 2024

  2024-10-08 · 21 citations

  articleOpen access1st authorCorresponding

  Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2024 workshop provides a venue for presenting and discussing the important features of the next generation of CARS and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.

  PublisherOA PDFDOI

Recent grants

• CAREER: Next Generation Personalization Technologies

  NSF · $450k · 2006–2012

Frequent coauthors

• Alok Gupta

  57 shared

• Mochen Yang

  Twin Cities Orthopedics

  52 shared

• Alexander Tuzhilin

  New York University

  49 shared

• Jesse Bockstedt

  Emory University

  46 shared

• Shawn P. Curley

  University of Minnesota System

  42 shared

• Jingjing Zhang

  Nanjing Forestry University

  31 shared

• Patrick J. O’Connor

  HealthPartners

  25 shared

• Julian Wolfson

  University of Minnesota

  23 shared

Awards & honors

• INFORMS Information Systems Society’s Distinguished Fellow A…
• Association for Information Systems Fellow Award