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Bart P. Knijnenburg
Researcher at Clemson University
Publications - 125
Citations - 3517
Bart P. Knijnenburg is an academic researcher from Clemson University. The author has contributed to research in topics: Recommender system & Information privacy. The author has an hindex of 29, co-authored 107 publications receiving 2722 citations. Previous affiliations of Bart P. Knijnenburg include Samsung & Eindhoven University of Technology.
Papers
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Journal ArticleDOI
Explaining the user experience of recommender systems
TL;DR: This paper proposes a framework that takes a user-centric approach to recommender system evaluation that links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively).
Proceedings ArticleDOI
Understanding choice overload in recommender systems
TL;DR: Investigation of the effect of recommendation set size and set quality on perceived variety, recommendation set attractiveness, choice difficulty and satisfaction with the chosen item shows that larger sets containing only good items do not necessarily result in higher choice satisfaction compared to smaller sets.
Journal ArticleDOI
Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems
Bart P. Knijnenburg,Alfred Kobsa +1 more
TL;DR: A unified approach to privacy decision research is described that describes the cognitive processes involved in users’ “privacy calculus” in terms of system-related perceptions and experiences that act as mediating factors to information disclosure.
Proceedings ArticleDOI
Each to his own: how different users call for different interaction methods in recommender systems
TL;DR: The results show that most users (and particularly domain experts) are most satisfied with a hybrid recommender that combines implicit and explicit preference elicitation, but that novices and maximizers seem to benefit more from a non-personalizedRecommender that just displays the most popular items.
Proceedings ArticleDOI
Inspectability and control in social recommenders
TL;DR: An online user experiment with a Facebook music recommender system that gives users control over the recommendations is performed, and the results show that inspectability and control indeed increase users' perceived understanding of and control of the system, their rating of the recommendation quality, and their satisfaction with the system.