Um método social-evolucionário para geração de rankings que apoiem a recomendação de eventos
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Data
2014-08-22
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Universidade Federal de Goiás
Resumo
With the development of web 2.0, social networks have achieved great space on the
internet, with that many users provide information and interests about themselves. There
are expert systems that make use of the user’s interests to recommend different products,
these systems are known as Recommender Systems. One of the main techniques of a
Recommender Systems is the Collaborative Filtering (User-based) which recommends
products to users based on what other similar people liked in the past. Therefore, this
work presents model approximation of functions that generates rankings, that through
a Genetic Algorithm, is able to learn an approximation function composed by different
social variables, customized for each Facebook user. The learned function must be able
to reproduce a ranking of people (friends) originally created with user’s information, that
apply some influence in the user’s decision. As a case study, this work discusses the
context of events through information regarding the frequency of participation of some
users at several distinct events. Two different approaches on learning and applying the
approximation function have been developed. The first approach provides a general model
that learns a function in advance and then applies it in a set of test data and the second
approach presents an specialist model that learns a specific function for each test scenario.
Two proposals for evaluating the ordering created by the learned function, called objective
functions A and B, where the results for both objective functions show that it is possible
to obtain good solutions with the generalist and the specialist approaches of the proposed
method.
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Citação
PASCOAL, L. M. L. Um método social-evolucionário para geração de rankings que apoiem a recomendação de eventos. 2014. 136 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2014.