Please use this identifier to cite or link to this item: http://dspace.utpl.edu.ec/handle/123456789/18775
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dc.contributor.authorValdiviezo Diaz, P.es_ES
dc.date.accessioned2017-06-16T22:02:20Z-
dc.date.available2016-03-29es_ES
dc.date.available2017-06-16T22:02:20Z-
dc.date.issued2016-07-25es_ES
dc.date.submitted08/06/2016es_ES
dc.identifier10.1109/CISTI.2016.7521413es_ES
dc.identifier.isbn21660727es_ES
dc.identifier.issn9.79E+16es_ES
dc.identifier.other10.1109/CISTI.2016.7521413es_ES
dc.identifier.urihttp://dspace.utpl.edu.ec/handle/123456789/18775-
dc.description.abstractThis research aims to use a hybrid recommendation method based on probabilistic techniques and topics modeling that provide recommendations most close fitting the user compared to other traditional recommendation models. We carry out a comprehensive review of the recommended methods for content-based systems and collaborative filtering, mainly in the domain of recommending movies. The methods discussed are the matrix factorization and Latent Dirichlet Allocation method. The literature review around these models focuses on identifying problems and open issues that may be covered for future researches. Also, we analyzed the recommendation models that integrant latent factor methods and topics modeling, which will be used to compare results obtained with the hybrid model.es_ES
dc.languageEspañoles_ES
dc.subjectfactorization matrixes_ES
dc.subjectLatent Dirichlet Allocationes_ES
dc.subjectrecommender systemtes_ES
dc.subjecttopic modeles_ES
dc.titleA comprehensive view of recommendation methods based on probabilistic techniques [Una Comprensiva Revisión de los Métodos de Recomendación basados en Técnicas Probabilísticas]es_ES
dc.typeArticlees_ES
dc.publisherIberian Conference on Information Systems and Technologies, CISTIes_ES
Appears in Collections:Artículos de revistas Científicas

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