PrefREC: uma metodologia para desenvolvimento de sistemas de recomendação utilizando algoritmos de mineração de preferências

• Ano de defesa: 2014
• Autor(a) principal: Oliveira, Cleiane Gonçalves
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Ciência da Computação; Ciências Exatas e da Terra; UFU
• Palavras-chave em Português: Sistemas de recomendação; Mineração de preferências; Preferências contextuais; Banco de dados; Mineração de dados (Computação); Recommender systems; Preferences mining; Contextual preferences; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufu.br/handle/123456789/12550; https://doi.org/10.14393/ufu.di.2014.27

The huge amount of information available on the web has bothered users to select items that meet their needs . The Recommender Systems emerged as indispensable tools in this information overload scenario in order to lter out what is of interest to the user and allow him to have a dierentiated experience with existing information systems. We present a methodology for developing recommendation systems, using mining algorithms preferences: PrefRec. We aim at building Recommender Systems that have good values of accuracy and allowing a more satisfying interaction to the user, from the validation measures of accuracy, coverage, novelty and serendipity. The use of algorithms for mining preferences objectively understand the user\'s preferences about the characteristics of the items, achieving more accurate recommendations. In the case study implemented, the Recommendation System XPrefRec, we apply a mining algorithm from a special type of contextual preferences in order to dene the user preferences on a certain context. We also analyzed what are the factors that inuence the methodology proposed in the Recommendation System performance and presents the comparison of the performance of this system with the state of the art regarding Hybrid Recommender Systems.