Text retrieval methods for item ranking in collaborative filtering
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2011Citation
10.1007/978-3-642-20161-5_30
Advances in Information Retrieval: 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings. Lecture Notes in Computer Science, Volumen 6611. Springer, 2011. 301-306.
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-642-20160-8 (print); 978-3-642-20161-5 (online)DOI
10.1007/978-3-642-20161-5_30Funded by
This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), and the Regional Government of Madrid (S2009TIC-1542)Project
Comunidad de Madrid. S2009/TIC-1542/MA2VICMREditor's Version
http://dx.doi.org/10.1007/978-3-642-20161-5_30Subjects
Information Storage and Retrieval; Database Management; Information Systems Applications; InformáticaNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-20161-5_30Proceedings of 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011.
Rights
© Springer-Verlag Berlin Heidelberg 2011Abstract
Collaborative Filtering (CF) aims at predicting unknown ratings of a user from other similar users. The uniqueness of the problem has made its formulation distinctive to other information retrieval problems. While the formulation has proved to be effective in rating prediction tasks, it has limited the potential connections between these algorithms and Information Retrieval (IR) models. In this paper we propose a common notational framework for IR and rating-based CF, as well as a technique to provide CF data with a particular structure, in order to be able to use any IR weighting function with it. We argue that the flexibility of our approach may yield to much better performing algorithms. In fact, in this work we have found that IR models perform well in item ranking tasks, along with different normalization strategies.
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Google Scholar:Bellogin Kouki, Alejandro
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Wang, Jun
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Castells Azpilicueta, Pablo
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