Probabilistic collaborative filtering with negative cross entropy
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
ACMDate
2013Citation
10.1145/2507157.2507191
RecSys '13: Proceedings of the 7th ACM conference on Recommender systems, ACM, 2013. 387-390
ISBN
978-1-4503-2409-0DOI
10.1145/2507157.2507191Funded by
This work was funded by Secretaría de Estado de Investigación, Desarrollo e Innovación from the Spanish Government under projects TIN2012-33867 and TIN2011-28538-C02.Editor's Version
http://dx.doi.org/10.1145/2507157.2507191Subjects
Collaborative filtering; Recommender systems; Relevance models; InformáticaNote
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in RecSys '13 Proceedings of the 7th ACM conference on Recommender systems, http://dx.doi.org/10.1145/2507157.2507191.Rights
© 2013 ACMAbstract
Relevance-Based Language Models are an effective IR approach which explicitly introduces the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models have shown to achieve state-of-the-art retrieval performance in the pseudo relevance feedback task. In this paper we propose a novel adaptation of this language modeling approach to rating-based Collaborative Filtering. In a memory-based approach, we apply the model to the formation of user neighbourhoods, and the generation of recommendations based on such neighbourhoods. We report experimental results where our method outperforms other standard memory-based algorithms in terms of ranking precision.
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Google Scholar:Bellogin Kouki, Alejandro
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Parapar, Javier
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Castells Azpilicueta, Pablo
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