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TP-TA: a comparative analytical framework for trust prediction models in online social networks based on trust aspects

TP-TA: a comparative analytical framework for trust prediction models in online social networks... Artif Intell Rev DOI 10.1007/s10462-017-9583-1 TP-TA: a comparative analytical framework for trust prediction models in online social networks based on trust aspects 1 1 Aynaz Khaksari · MohammadReza Keyvanpour © Springer Science+Business Media B.V. 2017 Abstract Formation of online social network (OSN) strongly depends on the quality of relationships between its agents. Such relationships are affected by a host of factors; trust is one of them. To enhance the quality of relationships in such networks, it is important to find a mechanism to predict the degree of trust among participating agents since trust is the major driving force for initiating and developing social relationships. Although much effort has been made to develop quantitative techniques to obtain trust value, there is a lack of coherent classification of such techniques to achieve a macro vision of trust prediction models and identify their strengths and weaknesses. In this paper, we proposed TP-TA, an analytical framework which consists of three main components: First, classification of various existing trust prediction models in terms of trust aspects in the context of OSNs. Besides, main ideas, prospects, and challenges of each approach are highlighted for further research in this field. Second, defining general criteria to analyze http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

TP-TA: a comparative analytical framework for trust prediction models in online social networks based on trust aspects

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References (28)

Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media B.V.
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Computer Science, general
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-017-9583-1
Publisher site
See Article on Publisher Site

Abstract

Artif Intell Rev DOI 10.1007/s10462-017-9583-1 TP-TA: a comparative analytical framework for trust prediction models in online social networks based on trust aspects 1 1 Aynaz Khaksari · MohammadReza Keyvanpour © Springer Science+Business Media B.V. 2017 Abstract Formation of online social network (OSN) strongly depends on the quality of relationships between its agents. Such relationships are affected by a host of factors; trust is one of them. To enhance the quality of relationships in such networks, it is important to find a mechanism to predict the degree of trust among participating agents since trust is the major driving force for initiating and developing social relationships. Although much effort has been made to develop quantitative techniques to obtain trust value, there is a lack of coherent classification of such techniques to achieve a macro vision of trust prediction models and identify their strengths and weaknesses. In this paper, we proposed TP-TA, an analytical framework which consists of three main components: First, classification of various existing trust prediction models in terms of trust aspects in the context of OSNs. Besides, main ideas, prospects, and challenges of each approach are highlighted for further research in this field. Second, defining general criteria to analyze

Journal

Artificial Intelligence ReviewSpringer Journals

Published: Oct 22, 2017

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