Professor Robin Burke conducts research in personalized recommender systems, a field he helped found and develop. His most recent projects explore fairness, accountability and transparency in recommendation through the integration of objectives from diverse stakeholders and the implementation of stakeholder choice and governance. Professor Burke is the author of more than 200 peer-reviewed articles in various areas of artificial intelligence including recommender systems, machine learning, and information retrieval. His work has received support from the National Science Foundation, the National Endowment for the Humanities, the Fulbright Commission, and the MacArthur Foundation, among others.
keywords
Recommender systems, Machine learning, Fairness and bias in recommender systems, Multistakeholder recommender systems, Digital humanities, Algorithmic governance.
Hybrid Recommendation in Heterogeneous Networks.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
49-60.
2014
User Partitioning Hybrid for Tag Recommendation.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
74-85.
2014
Hybrid systems for personalized recommendations.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
133-152.
2003
Ranking algorithms for costly similarity measures.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
105-117.
2001
