Uma aplicação de mineração de dados para recomendação social

The search of knowledge and its manipulation in companies, institutions or other organizations has become a challenge nowadays. Mostly due to two aspects: the large volume of information available and the difficulty in extracting the knowledge proper to each person (intellectual capital). This diffi...

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Detalhes bibliográficos
Autor: FEITOSA, Rodrigo Miranda
Formato: tesis de maestría
Estado:Versión publicada
Fecha de publicación:2013
País:Brasil
Recursos:Universidade Federal do Maranhão (UFMA)
Repositorio:Biblioteca Digital de Teses e Dissertações da UFMA
Idioma:portugués
OAI Identifier:oai:tede2:tede/1832
Acesso em linha:http://tedebc.ufma.br:8080/jspui/handle/tede/1832
Access Level:acceso abierto
Palavra-chave:Mineração de Dados
Rede Social Baseada em Localização e Recomendação Socia
Sistemas de Recomendação
Data Mining
Recommender Systems
Location-Based Social Networking and Social Recommendation
Sistemas de Informação
Descrição
Resumo:The search of knowledge and its manipulation in companies, institutions or other organizations has become a challenge nowadays. Mostly due to two aspects: the large volume of information available and the difficulty in extracting the knowledge proper to each person (intellectual capital). This difficulty becomes more accentuated when the scenario involved the extraction of knowledge is the Web. The area of Knowledge Management seeks a solution to the limitations described above. Techniques for extracting and control of knowledge can be adopted with the use of Artificial Intelligence, particularly the Knowledge Discovery in Databases. This work proposes the creation of a methodology and application that perform the Data Mining with textual information linked to geo data in a social network, in order to promote Social Recommendation. However, approaches in building recommendation systems present some shortcomings in filtering the results and the way they are suggested to users. The research aims to remedy these deficiencies and addresses issues that still need to search more effective and consolidated results.