Nearest neighbor searching and applications
Nearest neighbor searching and applications
Comparing feature-based and clique-based user models for movie selection
Proceedings of the third ACM conference on Digital libraries
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Multidimensional binary search trees used for associative searching
Communications of the ACM
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Rule induction and instance-based learning a unified approach
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A modal symbolic classifier for selecting time series models
Pattern Recognition Letters
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Recommender Systems use Information Filtering techniques to manage user preferences and provide the user with options, which will present greater possibility to satisfy them. Among these techniques, Content Based Filtering recommend new items by comparing them with a user profile, usually expressed as a set of items given by the user. This comparison is often performed using the k-NN method, which presents efficiency problems as the user profile grows. This paper presents an approach where each user profile is modeled by a meta-prototype and the comparison between an item and a profile is based on a suitable matching function. We show experimentally that our approach clearly outperforms the k-NN method while they presenting equal or even better prediction accuracy. The meta-prototype approach performs slightly worse than kd-tree speed up method but it exhibits a significant gain in prediction accuracy.