Abstract
User-generated content (UGC) from Internet users has significant value only when its credibility can be established. A basic approach to establishing credibility is to take an average of scores from annotators, while more sophisticated approaches have been used to eliminate anomalous scoring behaviour by giving different weights to scores from different annotator profiles. A number of applications such as file sharing and article reviewing use a decentralised architecture. While computing a weighted average of static values in a decentralised application is well studied, sophisticated UGC algorithms are more complicated since source values to be aggregated and their weights may change in time. In our work we consider a centralised credibility management algorithm, ScoreFinder, as an example, and show both structured and unstructured approaches for computing time-dependent weighted average values in peer-to-peer (P2P) networks. Experimental results on two real data sets demonstrate that our approaches converge and deliver results comparable to those from the centralised version of ScoreFinder.
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Liao, Y., Harwood, A., Ramamohanarao, K. (2010). Decentralisation of ScoreFinder: A Framework for Credibility Management on User-Generated Contents. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13672-6_27
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DOI: https://doi.org/10.1007/978-3-642-13672-6_27
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