Gedas Adomavicius

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

Selected publications

• Multistakeholder recommendation: Survey and research directions

  User Modeling and User-Adapted Interaction · 2020 · 296 citations

  DOI

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

  Information Systems Research · 2020 · 124 citations

  We develop a general-purpose agent-based simulation and modeling approach to analyze how user–recommender interactions affect recommender systems in the long run. Our explorations show that, over time, user–recommender interactions consistently lead to the longitudinal performance paradox of recommender systems. In particular, users’ reliance on recommendations, while helping users discover relevant items, actually hurts the future diversity of items that are recommended and consumed as well as…

  DOI

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

  INFORMS journal on computing · 2022 · 41 citations

  Because of the accessibility of big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many businesses, especially those in retail, because of the importance of forecasting in decision making. Improvement of forecasting accuracy, even by a small percentage, may have a substantial impact on companies’ production and financial planning, marketing strategies, inventory controls, and supply chain management. Specifically, ou…

  DOI

• Effects of Personalized Recommendations Versus Aggregate Ratings on Post-Consumption Preference Responses

  MIS Quarterly · 2022 · 24 citations

  1st authorCorresponding

  Online retailers use product ratings to signal quality and help consumers identify products for purchase. These ratings commonly take the form of either non-personalized, aggregate product ratings (i.e., the average rating a product received from a number of consumers such as “the average rating is 4.5/5 based on 100 reviews”), or personalized predicted preference ratings for a product (i.e., recommender-system-generated predictions for a consumer’s rating of a product such as “we think you’d ra…

  PublisherDOI

• Integrating Behavioral, Economic, and Technical Insights to Understand and Address Algorithmic Bias: A Human-Centric Perspective

  ACM Transactions on Management Information Systems · 2022 · 23 citations

  1st authorCorresponding

  Many important decisions are increasingly being made with the help of information systems that use artificial intelligence and machine learning models. These computational models are designed to discover useful patterns from large amounts of data, which augment human capabilities to make decisions in various application domains. However, there are growing concerns regarding the ethics challenges faced by these automated decision-making (ADM) models, most notably on the issue of algorithmic bias…

  PublisherDOI

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

Similar researchers at University of Minnesota

• Katie Andersonh-76
• Avner Ben-Nerh-45
• Michelle Duffyh-42