Relevance-based language modelling for recommender systems
EntityUAM. Departamento de Ingeniería Informática
10.1016/j.ipm.2013.03.001Information Processing & Management 49.4 (2013): 966 – 980
ISSN0306-4573 (print); 1873-5371 (online)
Funded byThis work was funded by Secretaría de Estado de Investigación, Desarrollo e Innovación from the Spanish Government under Projects TIN2012-33867 and TIN2011-28538-C02.
SubjectsCollaborative filtering; Probabilistic clustering; Recommender systems; Relevance models; Informática
NoteThis is the author’s version of a work that was accepted for publication in Journal Information Processing and Management: an International Journal. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal Information Processing and Management: an International Journal, 49, 4, (2013) DOI: 10.1016/j.ipm.2013.03.001
Rights© 2013 Elsevier B.V. All rights reserved
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the pseudo relevance feedback task. On the other hand, the field of recommender systems is a fertile research area where users are provided with personalised recommendations in several applications. In this paper, we propose an adaptation of the Relevance Modelling framework to effectively suggest recommendations to a user. We also propose a probabilistic clustering technique to perform the neighbour selection process as a way to achieve a better approximation of the set of relevant items in the pseudo relevance feedback process. These techniques, although well known in the Information Retrieval field, have not been applied yet to recommender systems, and, as the empirical evaluation results show, both proposals outperform individually several baseline methods. Furthermore, by combining both approaches even larger effectiveness improvements are achieved.
Google Scholar:Parapar, Javier - Bellogin Kouki, Alejandro - Castells Azpilicueta, Pablo - Barreiro, Álvaro
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