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dc.contributor.advisorCosta-Abreu, Márjory Da-
dc.contributor.authorBrito, Ana Caroline Medeiros-
dc.identifier.citationBRITO, Ana Caroline Medeiros. An empirical study of trope-based recommendation systems for animes. 2018. 55 f. Trabalho de Conclusão de Curso (Bacharelado em Ciência da Computação) - Universidade Federal do Rio Grande do Norte, Natal, 2018.pt_BR
dc.publisherUniversidade Federal do Rio Grande do Nortept_BR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Brazil*
dc.subjectRecommender Systemspt_BR
dc.subjectHybrid Systemspt_BR
dc.subjectJapanese animationpt_BR
dc.titleAn empirical study of trope-based recommendation systems for animespt_BR
dc.contributor.referees1Nunes, Isabel Dillmann-
dc.contributor.referees2Silva, Ivanovitch Medeiros Dantas da-
dc.description.resumoRecommender Systems is a fundamental field of study considering the big volume of information and data available everywhere - when we need to choose something to buy, to use, to experience. These systems have the purpose of help users in the task of choosing or even just suggesting some services. This work is an overview of the main approaches used to make a recommendation and an investigation about how features present in stories - called tropes - can be helpful to increase prediction quality. There were tested seven algorithms, two of them using tropes. We have tested said algorithms with a new database composed of Japanese animations and tropes that is also a contribution of this work. Some interesting conclusions were obtained analyzing the results in the new database proposed and the other one already known - MovieLens enriched with tropes -, the testes with tropes disclose no substantial positive impact in predictionspt_BR
dc.publisher.departmentCiência da Computaçãopt_BR
Appears in Collections:Ciência da Computação

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