Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Specifying preferences based on user history
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Meta-recommendation systems: user-controlled integration of diverse recommendations
Proceedings of the eleventh international conference on Information and knowledge management
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
User Involvement in Automatic Filtering: An Experimental Study
User Modeling and User-Adapted Interaction
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
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
Content-based music filtering system with editable user profile
Proceedings of the 2006 ACM symposium on Applied computing
Open user profiles for adaptive news systems: help or harm?
Proceedings of the 16th international conference on World Wide Web
Journal of Management Information Systems
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion
UM '07 Proceedings of the 11th international conference on User Modeling
Discovery-oriented collaborative filtering for improving user satisfaction
Proceedings of the 14th international conference on Intelligent user interfaces
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Although recommender systems have come to give recommendations with high precision, users are not always satisfied with the recommendations. User satisfaction is apparently influenced by many other factors. We specifically examined user intervention as one factor influencing user satisfaction. We tested two hypotheses: user intervention itself improves user satisfaction; and the more users intervene in the recommendation process, the more they are satisfied with the recommendations. We conducted an experiment incorporating user intervention of several kinds to reveal the relation between user intervention and user satisfaction.