dc.creatorCabral, Bruno
dc.creatorBeltrao, Renato Dompieri
dc.creatorManzato, Marcelo Garcia
dc.creatorDurão, Frederico Araújo
dc.date.accessioned2015-03-24T14:00:05Z
dc.date.accessioned2018-07-04T17:03:34Z
dc.date.available2015-03-24T14:00:05Z
dc.date.available2018-07-04T17:03:34Z
dc.date.created2015-03-24T14:00:05Z
dc.date.issued2014-11
dc.identifierBrazilian Symposium on Multimedia and the Web, 20th, 2014, João Pessoa.
dc.identifier9781450332309
dc.identifierhttp://www.producao.usp.br/handle/BDPI/48641
dc.identifierhttp://dx.doi.org/10.1145/2664551.2664569
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1644090
dc.description.abstractIn this paper, we analyze the application of ensemble algorithms to improve the ranking recommendation problem with multiple metadata. We propose three generic ensemble strategies that do not require modification of the recommender algorithm. They combine predictions from a recommender trained with distinct metadata into a unified rank of recommended items. The proposed strategies are Most Pleasure, Best of All and Genetic Algorithm Weighting. The evaluation using the HetRec 2011 MovieLens 2k dataset with five different metadata (genres, tags, directors, actors and countries) shows that our proposed ensemble algorithms achieve a considerable 7% improvement in the Mean Average Precision even with state-of-art collaborative filtering algorithms.
dc.languageeng
dc.publisherUniversidade Federal da Paraíba – UFPB
dc.publisherNúcleo de Pesquisa e Extensão em Aplicações de Vídeo Digital - LAViD
dc.publisherSociedade Brasileira de Computação – SBC
dc.publisherJoão Pessoa
dc.relationBrazilian Symposium on Multimedia and the Web, 20th
dc.rightsCopyright ACM
dc.rightsclosedAccess
dc.subjectDesign
dc.subjectAlgorithms
dc.subjectrecommendation
dc.subjectensemble
dc.subjectmetadata
dc.subjectmovie
dc.subjectcollaborative filtering
dc.titleCombining multiple metadata types in movies recommendation using ensemble algorithms
dc.typeActas de congresos


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