dc.creatorSantos Junior, Edson Benedito dos
dc.creatorGoularte, Rudinei
dc.creatorManzato, Marcelo Garcia
dc.date.accessioned2014-05-30T20:41:06Z
dc.date.accessioned2018-07-04T16:48:47Z
dc.date.available2014-05-30T20:41:06Z
dc.date.available2018-07-04T16:48:47Z
dc.date.created2014-05-30T20:41:06Z
dc.date.issued2014-03
dc.identifierSymposium on Applied Computing, 29th, 2014, Gyeongju.
dc.identifier9781450324694
dc.identifierhttp://www.producao.usp.br/handle/BDPI/45185
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1640708
dc.description.abstractIn this paper, we propose a recommender system approach which considers contextual information from users and items in order to improve the accuracy of a neighborhood-based collaborative filtering algorithm. One advantage of our model is the possibility to bias the users' similarity computation according to a contextual constraint, such as the group of individuals who share the same demographic information, or the set of users with whom the user is interacting at the moment. The proposal represents the first steps towards the development of a group recommender system model. We provide an evaluation of our method with the MovieLens dataset, and compare our approach against other known techniques reported in the literature.
dc.languageeng
dc.publisherAssociation for Computing Machinery - ACM
dc.publisherDongguk University
dc.publisherGyeongju
dc.relationSymposium on Applied Computing, 29th
dc.rightsCopyright ACM
dc.rightsclosedAccess
dc.subjectcollaborative filtering
dc.subjectneighborhood model
dc.subjectdemographic data
dc.titlePersonalized collaborative filtering: a neighborhood model based on contextual constraints
dc.typeActas de congresos


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