Opinion filtered recommendation trust model in peer-to-peer networks

Authors:
Weihua Song;Vir V. Phoha
Affiliations:
Computer Science, College of Engineering and Science, Louisiana Tech University, Ruston, LA;Computer Science, College of Engineering and Science, Louisiana Tech University, Ruston, LA
Venue:
AP2PC'04 Proceedings of the Third international conference on Agents and Peer-to-Peer Computing
Year:
2004

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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.