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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

ORFEL: Efficient detection of defamation or illegitimate promotion in online recommendation

Texto completo
Autor(es):
Gimenes, Gabriel ; Cordeiro, Robson L. F. ; Rodrigues-, Jr., Jose F.
Número total de Autores: 3
Tipo de documento: Artigo Científico
Fonte: INFORMATION SCIENCES; v. 379, p. 274-287, FEB 10 2017.
Citações Web of Science: 4
Resumo

What if a successful company starts to receive a torrent of low-valued (one or two stars) recommendations in its mobile apps from multiple users within a short (say one month) period of time? Is it legitimate evidence that the apps have lost in quality, or an intentional plan (via lockstep behavior) to steal market share through defamation? In the case of a systematic attack to one's reputation, it might not be possible to manually discern between legitimate and fraudulent interaction within the huge universe of possibilities of user-product recommendation. Previous works have focused on this issue, but none of them took into account the context, modeling, and scale that we consider in this paper. Here, we propose the novel method Online-Recommendation Fraud ExcLuder (ORFEL) to detect defamation and/or illegitimate promotion of online products by using vertex-centric asynchronous parallel processing of bipartite (users-products) graphs. With an innovative algorithm, our results demonstrate both efficacy and efficiency over 95% of potential attacks were detected, and ORFEL was at least two orders of magnitude faster than the state-of-the-art. Over a novel methodology, our main contributions are: (1) a new algorithmic solution; (2) one scalable approach; and (3) a novel context and modeling of the problem, which now addresses both defamation and illegitimate promotion. Our work deals with relevant issues of the Web 2.0, potentially augmenting the credibility of online recommendation to prevent losses to both customers and vendors. (C) 2016 Elsevier Inc. All rights reserved. (AU)

Processo FAPESP: 13/10026-7 - Análise de grafos baseada em processamento paralelo assíncrono centrado em vértices: aplicações em dados de escala planetária
Beneficiário:Gabriel Perri Gimenes
Modalidade de apoio: Bolsas no Brasil - Mestrado
Processo FAPESP: 14/21483-2 - Divisão relacional por similaridade em banco de dados
Beneficiário:Robson Leonardo Ferreira Cordeiro
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 16/02557-0 - Processamento analítico de grandes grafos: identificação de padrões para o suporte à decisão na Web 2.0
Beneficiário:José Fernando Rodrigues Júnior
Modalidade de apoio: Auxílio à Pesquisa - Regular