Abstract
In the Internet era the information overload and the challenge to detect quality content has raised the issue of how to rank both resources and users in online communities. In this paper we develop a general ranking method that can simultaneously evaluate users’ reputation and objects’ quality in an iterative procedure, and that exploits the trust relationships and social acquaintances of users as an additional source of information. We test our method on two real online communities, the EconoPhysics forum and the Last.fm music catalogue, and determine how different variants of the algorithm influence the resultant ranking. We show the benefits of considering trust relationships, and define the form of the algorithm better apt to common situations.
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Liao, H., Cimini, G., Medo, M. (2012). Measuring Quality, Reputation and Trust in Online Communities. In: Chen, L., Felfernig, A., Liu, J., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2012. Lecture Notes in Computer Science(), vol 7661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34624-8_46
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DOI: https://doi.org/10.1007/978-3-642-34624-8_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34623-1
Online ISBN: 978-3-642-34624-8
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