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dc.contributor.authorFernández, Diego
dc.contributor.authorFormoso, Vreixo
dc.contributor.authorCacheda, Fidel
dc.contributor.authorCarneiro, Víctor
dc.date.accessioned2024-06-13T11:35:18Z
dc.date.available2024-06-13T11:35:18Z
dc.date.issued2020-12
dc.identifier.citationD. Fernández, V. Formoso, F. Cacheda, and V. Carneiro, "A Content-Based Approach to Profile Expansion", International Journal of Uncertainty, Fuzziness and Knowldege-Based Systems, Vol. 28, Issue 6, pp. 981 - 1002, December 2020. doi: 10.1142/S0218488520500385es_ES
dc.identifier.urihttp://hdl.handle.net/2183/36892
dc.description.abstract[Abstract]: Collaborative Filtering algorithms suffer from the so-called cold-start problem. In particular, when a user has rated few items, recommendations offered by these algorithms are not too accurate. Profile Expansion techniques have been described as a way to tackle this problem without bothering the user with additional information requests by increasing automatically the size of the user profile. Up to now, only collaborative approaches had been proposed for Profile Expansion. However, content-based techniques can also be used. We perform a manual analysis of a movie dataset to analyze how content features behave. According to this analysis, we propose a content-based approach, which is also combined with collaborative information. Concretely, we expose the advantages and disadvantages of the combination with a popularity feature. Moreover, a comparison to pure collaborative approaches is performed. Our approach is evaluated in a new system situation. That is, not only the active user has few ratings, but also most of the users. The results show that content-based information is useful for rating prediction. In addition, recommendations are less personalized as popularity feature acquires more relevance for item selection.es_ES
dc.description.sponsorshipThis study was supported by the Ministry of Economy and Competitiveness of Spain and FEDER funds of the European Union (Project TIN2015-70648-P), by the Xunta de Galicia (Singular research center of Galicia, accreditation ED431G/01 2016-2019) and the European Union (European Regional Development Fund). This research was supported by the Ministry of Science and Innovation, Spain’s National Research and Development Plan, through the PID2019-111388GB-I00 project.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01 2016-2019es_ES
dc.language.isoenges_ES
dc.publisherWorld Scientifices_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2015-70648-P/ES/TECNICAS DE INTELIGENCIA COLECTIVA PARA LA GESTION DE AMENAZAS EN REDES Y SISTEMAS Tes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111388GB-I00/ES/DETECCION TEMPRANA DE INTRUSIONES Y ANOMALIAS EN REDES DEFINIDAS POR SOFTWARE/es_ES
dc.relation.urihttps://doi.org/10.1142/S0218488520500385es_ES
dc.rightsAttribution-NonCommercial-NoDerivs 4.0 (CC BY- NC-ND)es_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectCollaborative filteringes_ES
dc.subjectcontent-basedes_ES
dc.subjectprofile expansiones_ES
dc.subjectcold-startes_ES
dc.titleA Content-Based Approach to Profile Expansiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleInternational Journal of Uncertainty, Fuzziness and Knowldege-Based Systemses_ES
UDC.volume28es_ES
UDC.issue6es_ES
UDC.startPage981es_ES
UDC.endPage1002es_ES
dc.identifier.doi10.1142/S0218488520500385


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