320 citations · 368 across the 11 of their papers we have counts for
11 papers
Recommending Composite Items Using Multi-Level Preference Information: A Joint Interaction Modeling Approach
Xuan Bi, Yaqiong Wang, Gediminas Adomavicius +1
With the advancement of machine learning and artificial intelligence technologies, recommender systems have been increasingly used across a vast variety of platforms to efficiently…
Robustness is Important: Limitations of LLMs for Data Fitting
Hejia Liu, Mochen Yang, Gediminas Adomavicius
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…
De-centering the (Traditional) User: Multistakeholder Evaluation of Recommender Systems
Robin Burke, Gediminas Adomavicius, Toine Bogers +7
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…
EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference with ML-Generated Variables
Gordon Burtch, Edward McFowland, Mochen Yang +1
Despite increasing popularity in empirical studies, the integration of machine learning generated variables into regression models for statistical inference suffers from the measur…
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…
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 +5
Models for predicting the risk of cardiovascular events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used ri…
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data
Julian Wolfson, Sunayan Bandyopadhyay, Mohamed Elidrisi +5
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts…