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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References
Pujol, J., Sang, R., Halberstadt, A.: Extracting reputation in multi agent systems by means of social network topology. In: Proceedings of the first International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2002 (2002)
Mui, L., Mohtashemi, M., Halberstadt, A.: A computational model for trust and reputation. In: Proc. 35th Hawaii International Conference on System Sciences (2002)
Wang, Y., Vassileva, J.: Beyesian network-based trust model. In: Proc. of IEEE/WIC International Conference on Web Intelligence, WI 2003 (2003)
Yu, B., Singh, M.P.: Towards a probabilistic model of distributed reputation management. In: Proc. 4th Workshop on Deception, Fraud and Trust in Agent Societies (2001)
Zacharia, G., Mae, P.: Collaborative reputation mechanisms in electronic marketplaces. In: Proc. 32nd Hawaii International Conf. on System Sciences (1999)
Riggs, T., Wilensky, R.: An algorithm for automated rating of reviewers. In: Proceedings of first ACM and IEEE Joint Conference on Digital Libraries, JCDL 2001 (2001)
Azzedin, F., Maheswaran, M.: Evolving and managing trust in grid computing systems. In: Proceedings of IEEE Canadian Conference on Electrical & Computer Engineering, CCECE 2002 (2002)
Daniani, E., Vimercati, S., Paraboschi, S., Samarati, P., Violante, F.: A reputation-based approach for choosing reliable resources in peer-to-peer networks. In: Proceedings of the 9th ACM Conference on Computer and Communications Security, CCS 2002 (2002)
Gupta, M., Judge, P., Ammar, M.: A reputation system for peer-to-peer networks. In: Proceedings of the 13th ACM International workshop on Network and Operating Systems Support for Design Audio and Vedio, NOSSDAV 2003 (2003)
Kamvar, S., Schlosser, M., Garcia-Molina, H.: The eigentrust algorithm for reputation management in p2p networks. In: Proceedings of the twelfth ACM International World Wide Web Conference, WWW 2003 (2003)
Schafer, J.B., Konstan, J., Riedl, J.: Meta-recommendation systems: User-controlled integration of diverse recommendations. In: Proceedings of ACM Conference on Information and Knowledge Management, CIKM 2002 (2002)
Yu, B., Singh, M.P.: Detecting deception in reputation management. In: Faratin, P., Parkes, D.C., RodrÃguez-Aguilar, J.-A., Walsh, W.E. (eds.) AMEC 2003. LNCS (LNAI), vol. 3048, Springer, Heidelberg (2004)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
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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
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