Mañana, JUEVES, 24 DE ABRIL, el sistema se apagará debido a tareas habituales de mantenimiento a partir de las 9 de la mañana. Lamentamos las molestias.
A performance prediction approach to enhance collaborative filtering performance
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2010Citation
10.1007/978-3-642-12275-0_34
Advances in Information Retrieval: 32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28-31, 2010.Proceedings. Lecture Notes in Computer Science, Volumen 5993. Springer, 2010. 382-393.
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-642-12274-3 (print); 978-3-642-12275-0 (online)DOI
10.1007/978-3-642-12275-0_34Funded by
This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02) and the Ministry of Industry, Tourism and Commerce (CENIT-2007-1012)Editor's Version
http://dx.doi.org/10.1007/978-3-642-12275-0_34Subjects
Data Mining and Knowledge Discovery; Information Storage and Retrieval; Database Management; Information Systems Applications; Multimedia Information Systems; InformáticaNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-12275-0_34Proceedings of 32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28-31, 2010.
Rights
© Springer-Verlag Berlin Heidelberg 2010Abstract
Performance prediction has gained increasing attention in the IR field since the half of the past decade and has become an established research topic in the field. The present work restates the problem in the area of Collaborative Filtering (CF), where it has barely been researched so far. We investigate the adaptation of clarity-based query performance predictors to predict neighbor performance in CF. A predictor is proposed and introduced in a kNN CF algorithm to produce a dynamic variant where neighbor ratings are weighted based on their predicted performance. The properties of the predictor are empirically studied by, first, checking the correlation of the predictor output with a proposed measure of neighbor performance. Then, the performance of the dynamic kNN variant is examined on different sparsity and neighborhood size conditions, where the variant consistently outperforms the baseline algorithm, with increasing difference on small neighborhoods.
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Google Scholar:Bellogin Kouki, Alejandro
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Castells Azpilicueta, Pablo
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