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dc.contributor.authorFernández, Diego
dc.contributor.authorFormoso, Vreixo
dc.contributor.authorCacheda, Fidel
dc.contributor.authorCarneiro, Victor
dc.date.accessioned2020-06-05T13:58:38Z
dc.date.available2020-06-05T13:58:38Z
dc.date.issued2019-11-07
dc.identifier.citationFernández D, Formoso V, Cacheda F, Carneiro V (2019) High Order Profile Expansion to tackle the new user problem on recommender systems. PLoS ONE 14(11): e0224555. https://doi.org/10.1371/journal.pone.0224555es_ES
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/2183/25687
dc.descriptionData Availability: The complete dataset for the High Order Profile Expansion experiments has been published in the public repository: https://doi.org/10.6084/m9.figshare.9798155.es_ES
dc.description.abstract[Abstract] Collaborative Filtering algorithms provide users with recommendations based on their opinions, that is, on the ratings given by the user for some items. They are the most popular and widely implemented algorithms in Recommender Systems, especially in e-commerce, considering their good results. However, when the information is extremely sparse, independently of the domain nature, they do not present such good results. In particular, it is difficult to offer recommendations which are accurate enough to a user who has just arrived to a system or who has rated few items. This is the well-known new user problem, a type of cold-start. Profile Expansion techniques had been already presented as a method to alleviate this situation. These techniques increase the size of the user profile, by obtaining information about user tastes in distinct ways. Therefore, recommender algorithms have more information at their disposal, and results improve. In this paper, we present the High Order Profile Expansion techniques, which combine in different ways the Profile Expansion methods. The results show 110% improvement in precision over the algorithm without Profile Expansion, and 10% improvement over Profile Expansion techniques.es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; TIN2015-70648-Pes_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01 2016-2019es_ES
dc.language.isoenges_ES
dc.publisherPublic Library of Sciencees_ES
dc.relation.urihttps://doi.org/10.1371/journal.pone.0224555es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectFiltering algorithmses_ES
dc.subjectRecommender systemses_ES
dc.subjecte-commercees_ES
dc.titleHigh Order Profile Expansion to tackle the new user problem on recommender systemses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitlePLoS ONEes_ES
UDC.volume14es_ES
UDC.issue11es_ES
dc.identifier.doihttps://doi.org/10.6084/m9.figshare.9798155.


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