On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Enhancing digital libraries with TechLens+
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Impacts of user privacy preferences on personalized systems: a comparative study
Designing personalized user experiences in eCommerce
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Evaluating product search and recommender systems for E-commerce environments
Electronic Commerce Research
User-centered evaluation of adaptive and adaptable systems: A literature review
The Knowledge Engineering Review
Proceedings of the third ACM conference on Recommender systems
The effect of preference elicitation methods on the user experience of a recommender system
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Understanding choice overload in recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Correlating perception-oriented aspects in user-centric recommender system evaluation
Proceedings of the 4th Information Interaction in Context Symposium
Conducting user experiments in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm
Proceedings of the 2013 conference on Computer supported cooperative work
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As recommender systems are increasingly deployed in the real world, they are not merely tested offline for precision and coverage, but also "online" with test users to ensure good user experience. The user evaluation of recommenders is however complex and resource-consuming. We introduce a pragmatic procedure to evaluate recommender systems for experience products with test users, within industry constraints on time and budget. Researchers and practitioners can employ our approach to gain a comprehensive understanding of the user experience with their systems.