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dc.contributor.author | Picón, Artzai![]() |
es_ES |
dc.contributor.author | Rodríguez-Vaamonde, Sergio![]() |
es_ES |
dc.contributor.author | Jaén Martínez, Francisco Javier![]() |
es_ES |
dc.contributor.author | Mocholí Agües, Juan Bautista![]() |
es_ES |
dc.contributor.author | García, David![]() |
es_ES |
dc.contributor.author | Cadenas, Alejandro![]() |
es_ES |
dc.date.accessioned | 2014-02-05T09:55:22Z | |
dc.date.issued | 2012-01 | |
dc.identifier.issn | 0957-4174 | |
dc.identifier.uri | http://hdl.handle.net/10251/35358 | |
dc.description.abstract | [EN] Mobile devices are undergoing great advances in recent years allowing users to access an increasing number of services or personalized applications that can help them select the best restaurant, locate certain shops, choose the best way home or rent the best film. However this great quantity of services does not require the user to find and select those services needed for each specific situation. The classical approaches link some preferences to certain services, include the recommendations given by other users or even include certain fixed rules in order to choose the most appropriate services. However, since these methods assume that user needs can be modelled by fixed rules or preferences, they fail when modelling different users or makes them difficult to train. In this paper we propose a new algorithm that learns from the user's actions in different contextual situations, which allows to properly infer the most appropriate recommendations for a user in a specific contextual situation. This model, by using of a double knowledge diffusion approach, has been specifically designed to face the inherent lack of learning evidences, computational cost and continuous training requirements and, therefore, overcomes the performance and convergence rates offered by other learning methodologies. © 2011 Elsevier Ltd. All rights reserved. | es_ES |
dc.description.sponsorship | This work is partially underwritten by the Ministry of Industry, Tourism and Trade of the Government of Spain under the research project CENIT-2008-1019 | |
dc.format.extent | 7 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Expert Systems with Applications | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Context awareness | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Mobility | es_ES |
dc.subject | Personalisation | es_ES |
dc.subject | Services | es_ES |
dc.subject | User preferences | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | A statistical recommendation model of mobile services based on contextual evidences | es_ES |
dc.type | Artículo | es_ES |
dc.embargo.lift | 10000-01-01 | |
dc.embargo.terms | forever | es_ES |
dc.identifier.doi | 10.1016/j.eswa.2011.07.056 | |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//CENIT-2008-1019/ES/MIO!: TECNOLOGÍAS PARA PRESTAR SERVICIOS EN MOVILIDAD EN EL FUTURO UNIVERSO INTELIGENTE/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | Picón, A.; Rodríguez-Vaamonde, S.; Jaén Martínez, FJ.; Mocholí Agües, JB.; García, D.; Cadenas, A. (2012). A statistical recommendation model of mobile services based on contextual evidences. Expert Systems with Applications. 39(1):647-653. https://doi.org/10.1016/j.eswa.2011.07.056 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.eswa.2011.07.056 | es_ES |
dc.description.upvformatpinicio | 647 | es_ES |
dc.description.upvformatpfin | 653 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 39 | es_ES |
dc.description.issue | 1 | es_ES |
dc.relation.senia | 229984 | |
dc.contributor.funder | Ministerio de Industria, Turismo y Comercio |