320 citations · 368 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…