Bart Knijnenburg

28 Followers

Researcher on privacy decision-making and recommender systems. Our online lives are full of small but difficult decisions. Which app should I install? Should I post this on Facebook or not? Which YouTube video should I watch? What will this e-commerce website do with my personal information? In my research I try to understand the psychological principles behind these online decisions. Using Big Data and Machine Learning principles, I try to make these decisions a little easier with better user interfaces and "smart defaults".

privacy privacy calculus user experience recommender systems user experiments adaptive systems decision- making pps soups2014 location-sharing services information systems iui2013 recsys2012 personalization information disclosure recsys2011 social networks facebook icis2013 recsys2013 decision support system research communication ubicomp13 chi2013 human-computer interaction psychology big data usability machine learning decision-making nudge cscw2013 persuasion justification netflix dexa 2010 ec-web

Präsentationen
Dokumente
Infografiken

Aktuellste Beliebteste SlideShares

Bart Knijnenburg

Profiling Facebook Users' Privacy Behaviors

Information Disclosure Profiles for Segmentation and Recommendation

Counteracting the negative effect of form auto-completion on the privacy calculus

Simplifying Privacy Decisions: Towards Interactive and Adaptive Solutions

FYI: Communication Style Preferences Underlie Differences in Location-Sharing Adoption and Usage

Preference-based Location Sharing: Are More Privacy Options Really Better?

Big data - A critical appraisal

Helping Users with Information Disclosure Decisions: Potential for Adaptation (IUI2013 presentation)

CSCW networked privacy workshop - keynote

Hcsd talk ibm

Inspectability and Control in Social Recommenders

Tutorial on Conducting User Experiments in Recommender Systems

Privacy in Mobile Personalized Systems - The Effect of Disclosure Justifications

Explaining the User Experience of Recommender Systems with User Experiments

Using latent features diversification to reduce choice difficulty in recommendation lists

Recsys2011 presentation "Each to his own - How Different Users Call for Different Interaction Methods"

Recommendations and Feedback - The user-experience of a recommender system