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Master's Dissertation
DOI
10.11606/D.45.2011.tde-28092012-150655
Document
Author
Full name
Tássia Camões Araujo
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2011
Supervisor
Committee
Mandel, Arnaldo (President)
Gerosa, Marco Aurélio
Geus, Paulo Lício de
Title in Portuguese
AppRecommender: um recomendador de aplicativos GNU/Linux
Keywords in Portuguese
aplicativos
Debian GNU/Linux.
distribuições GNU/Linux
filtragem colaborativa
pacotes Debian
Sistemas de recomendação.
Abstract in Portuguese
A crescente oferta de programas de código aberto na rede mundial de computadores expõe potenciais usuários a muitas possibilidades de escolha. Em face da pluralidade de interesses desses indivíduos, mecanismos eficientes que os aproximem daquilo que buscam trazem benefícios para eles próprios, assim como para os desenvolvedores dos programas. Este trabalho apresenta o AppRecommender, um recomendador de aplicativos GNU/Linux que realiza uma filtragem no conjunto de programas disponíveis e oferece sugestões individualizadas para os usuários. Tal feito é alcançado por meio da análise de perfis e descoberta de padrões de comportamento na população estudada, de sorte que apenas os aplicativos considerados mais suscetíveis a aceitação sejam oferecidos aos usuários.
Title in English
AppRecommender: a recommender system for GNU/Linux applications
Keywords in English
applications
collaborative filtering
Debian GNU/Linux.
Debian packages
GNU/Linux distributions
Recommender systems
Abstract in English
The increasing availability of open source software on the World Wide Web exposes potential users to a wide range of choices. Given the individuals plurality of interests, mechanisms that get them close to what they are looking for would benefit users and software developers. This work presents AppRecommender, a recommender system for GNU/Linux applications which performs a filtering on the set of available software and individually offers suggestions to users. This is achieved by analyzing profiles and discovering patterns of behavior of the studied population, in a way that only those applications considered most prone to acceptance are presented to users.
 
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Publishing Date
2012-10-02
 
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