Relevance-based language modelling for recommender systems
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Elsevier BVDate
2013-07Citation
10.1016/j.ipm.2013.03.001
Information Processing & Management 49.4 (2013): 966 – 980
ISSN
0306-4573 (print); 1873-5371 (online)DOI
10.1016/j.ipm.2013.03.001Funded 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/10.1016/j.ipm.2013.03.001Subjects
Collaborative filtering; Probabilistic clustering; Recommender systems; Relevance models; InformáticaNote
This 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.2013.03.001Rights
© 2013 Elsevier B.V. All rights reserved
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Abstract
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.
Files in this item
Google Scholar:Parapar, Javier
-
Bellogin Kouki, Alejandro
-
Castells Azpilicueta, Pablo
-
Barreiro, Álvaro
This item appears in the following Collection(s)
Related items
Showing items related by title, author, creator and subject.