25 citations · 37 across the 4 of their papers we have counts for
5 papers
Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation
Gediminas Adomavicius, Dietmar Jannach, Stephan Leitner +1
Today's research in recommender systems is largely based on experimental designs that are static in a sense that they do not consider potential longitudinal effects of providing re…
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
Mochen Yang, Edward McFowland, Gordon Burtch +1
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predi…
Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness
Xuan Bi, Gediminas Adomavicius, William Li +1
Due to accessible big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many companies especially in th…
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke +5
Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tas…
Price and Profit Awareness in Recommender Systems
Dietmar Jannach, Gediminas Adomavicius
Academic research in the field of recommender systems mainly focuses on the problem of maximizing the users' utility by trying to identify the most relevant items for each user. Ho…