dc.creatorMacias, Mervyn
dc.creatorDe La Rosa, Freddy
dc.creatorAbad, Cristina
dc.date2009-10-19
dc.date2009-10-19
dc.date2009-10-19
dc.date.accessioned2023-08-08T22:21:27Z
dc.date.available2023-08-08T22:21:27Z
dc.identifierhttp://www.dspace.espol.edu.ec/handle/123456789/7756
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8091259
dc.descriptionA system of recommendations is a specific type of filter of information that helps the user to select such articles of his (her, your) interest as movies, musical, web pages, magazines, books, etc. Nowadays, the web sites that give these services need that the great quantity of information got for all the implicit or explicit actions of million users on million articles, is tried in a rapid way and with the minor possible infrastructure, this in order to obtain rapid and better indexes of useful preferences and to minor cost The Present work has as aim to present two alternatives of processing recommendation of musical articles based on the implicit preferences of the users and using a model of massive and scalable programming inside Hadoop's framework as a system of the execution of tasks in parallel and tolerantly to failures.
dc.formatapplication/pdf
dc.languagespa
dc.rightsopenAccess
dc.subjectFILTRADO COLABORATIVO
dc.subjectSISTEMA DE ARCHIVOS DISTRIBUIDOS HADOOP (HDFS)
dc.subjectMAHOUT
dc.subjectCOEFICIENTE CORRELACIÓN DE PEARSON.
dc.titleRecomendaciones con filtrado colaborativo basado en usuarío y en ítem aplicando el paradigma map-reduce
dc.typeArticle


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