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dc.contributor.authorBobadilla, Jesús
dc.contributor.authorGutiérrez, Abraham
dc.contributor.authorAlonso, Santiago
dc.contributor.authorHurtado, Remigio
dc.date2020-06
dc.date.accessioned2022-03-28T08:37:43Z
dc.date.available2022-03-28T08:37:43Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12730
dc.description.abstractIn the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 6, nº 2
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2758es_ES
dc.rightsopenAccesses_ES
dc.subjectrecommendation systemses_ES
dc.subjectclusteringes_ES
dc.subjectcollaborative filteringes_ES
dc.subjectdimensionality reductiones_ES
dc.subjectgroup recommendationes_ES
dc.subjectIJIMAIes_ES
dc.titleA Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groupses_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2020.03.002


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