REFINING THE TRUSTWORTHINESS ASSESSMENT OF SUPPLIERS THROUGH EXTRACTION OF STEREOTYPES

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2010

Autores

urbano, j (Autor) Outra; rocha, ap (Autor) FEUP; oliveira, e (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings of the 12th International Conference on Enterprise Information Systems

Páginas: 85-92

ICEIS 10

Funchal, Madeira, Portugal, 8 - 12 June, 2010

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 5 Citações

Classificação Científica

CORDIS: Ciências Físicas > Ciência de computadores

Outras Informações

ID Authenticus: P-007-WGZ

Abstract (EN): Trust management is nowadays considered a promising enabler technology to extend the automation of the supply chain to the search, evaluation and selection of suppliers located world-wide. Current agent-based Computational Trust and Reputation (CTR) systems concern the representation, dissemination and aggregation of trust evidences for trustworthiness assessment, and some recent proposals are moving towards situation-aware solutions that allow the estimation of trust when the information about a given supplier is scarce or even null. However, these enhanced, situation-aware proposals rely on ontology-like techniques that are not fine grained enough to detect light, but relevant, tendencies on supplier's behaviour. In this paper, we propose a technique that allows the extraction of positive and negative tendencies of suppliers in the fulfilment of established contracts. This technique can be used with any of the existing "traditional" CTR systems, improving their ability in selectively selecting a partner based on the characteristics of the situation in evaluation. In this paper, we test our proposal using an aggregation engine that embeds important properties of the dynamics of trust building.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8