Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Active Learning with Local Models
Neural Processing Letters
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Selective sampling for nearest neighbor classifiers
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
PTV: Intelligent Personalised TV Guides
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
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Recommender Systems enhance user access to relevant items {information, product} by using techniques, such as collaborative and content-based filtering, to select items according to the users personal preferences. Despite the success perspective, the acquisition of these preferences is usually the bottleneck for the practical use of this systems. Active learning approach could be used to minimize the number of requests for user evaluations but the available techniques cannot be applied to collaborative filtering in a straightforward manner. In this paper we propose an original active learning method, named ActiveCP, applied to KNN-based Collaborative Filtering. We explore the concepts of item's controversy and popularity within a given community of users to select the more informative items to be evaluated by a target user. The experiments testifies that ActiveCP allows the system to learn fast about each user preference, decreasing the required number of evaluations while keeping the precision of the recommendations.