Abstract
A multiagent distributed system consists of a network of heterogeneous peers of different trust evaluation standards. A major concern is how to form a requester’s own trust opinion of an unknown party from multiple recommendations, and how to detect deceptions since recommenders may exaggerate their ratings. This paper presents a novel application of neural networks in deriving personalized trust opinion from heterogeneous recommendations. The experimental results showed that a three-layered neural network converges at an average of 12528 iterations and 93.75% of the estimation errors are less than 5%. More important, the model is adaptive to trust behavior changes and has robust performance when there is high estimation accuracy requirement or when there are deceptive recommendations.
This work is supported in part by the Army Research Office under Grant No. DAAD 19-01-1-0646 and by Louisiana Board of Regents under Grant LEQSF(2003-05)-RD-A-17.
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Song, W., Phoha, V.V. (2005). Opinion Filtered Recommendation Trust Model in Peer-to-Peer Networks. In: Moro, G., Bergamaschi, S., Aberer, K. (eds) Agents and Peer-to-Peer Computing. AP2PC 2004. Lecture Notes in Computer Science(), vol 3601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11574781_23
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DOI: https://doi.org/10.1007/11574781_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29755-0
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