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Ali Hamzeh ;Ciência e Natura 2015, 37 6-2

Autor: Elnaz Ghodousi

Fuente: http://www.redalyc.org/articulo.oa?id=467547683012


Introducción



Ciência e Natura ISSN: 0100-8307 cienciaenaturarevista@gmail.com Universidade Federal de Santa Maria Brasil Ghodousi, Elnaz; Hamzeh, Ali A New Approach for Trust Prediction by using collaborative filtering based of Pareto dominance in Social Networks Ciência e Natura, vol.
37, núm.
6-2, 2015, pp.
95-101 Universidade Federal de Santa Maria Santa Maria, Brasil Available in: http:--www.redalyc.org-articulo.oa?id=467547683012 How to cite Complete issue More information about this article Journals homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative 95 Ciência e Natura, v.
37 Part 2 2015, p.
95−101 ISSN impressa: 0100-8307 ISSN on-line: 2179-460X A New Approach for Trust Prediction by using collaborative filtering based of Pareto dominance in Social Networks Elnaz Ghodousi1*, Ali Hamzeh2 1 Computer Science and Engineering Dept, Shiraz University, Shiraz, Iran, M.
Sc.
student e.ghodousi@shirazu.ac.ir 2 Computer Science and Engineering Dept, Shiraz University, Shiraz, Iran, Ph.
D ali@cse.shirazu.ac.ir Abstract Along with the increasing popularity of social web sites, users rely more on the trustworthiness information for many online activities among users.[24] However, such social network data often suffers from two problems, (1)severe data sparsity and are not able to provide users with enough information, (2)dataset’s is very large. Therefore, trust prediction has emerged as an important topic in social network research.
In this paper we proposed a new approach by using collaborative filtering method and the concept of Pareto dominance.
We uses Pareto dominance to perform a pre-filtering process eliminating less representative users from the k-neighbour selection process while retaining the most promising ones.
The results from experiments performed on FilmTrust dataset and Epinions dataset. Keyw...





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