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Mathematical Problems in Engineering
Volume 2013, Article ID 391720, 11 pages
http://dx.doi.org/10.1155/2013/391720
Research Article

The Reputation Evaluation Based on Optimized Hidden Markov Model in E-Commerce

1DISP Laboratory, University Lumiere of Lyon 2, France College of Information Science and Technology, Chengdu University, Chengdu, China
2College of Information Science and Technology, Chengdu University, Chengdu 610106, China
3Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan, Chengdu 610106, China

Received 24 March 2013; Accepted 21 June 2013

Academic Editor: Yi-Chung Hu

Copyright © 2013 Liu Chang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Nowadays, a large number of reputation systems have been deployed in practical applications or investigated in the literature to protect buyers from deception and malicious behaviors in online transactions. As an efficient Bayesian analysis tool, Hidden Markov Model (HMM) has been used into e-commerce to describe the dynamic behavior of sellers. Traditional solutions adopt Baum-Welch algorithm to train model parameters which is unstable due to its inability to find a globally optimal solution. Consequently, this paper presents a reputation evaluation mechanism based on the optimized Hidden Markov Model, which is called PSOHMM. The algorithm takes full advantage of the search mechanism in Particle Swarm Optimization (PSO) algorithm to strengthen the learning ability of HMM and PSO has been modified to guarantee interval and normalization constraints in HMM. Furthermore, a simplified reputation evaluation framework based on HMM is developed and applied to analyze the specific behaviors of sellers. The simulation experiments demonstrate that the proposed PSOHMM has better performance to search optimal model parameters than BWHMM, has faster convergence speed, and is more stable than BWHMM. Compared with Average and Beta reputation evaluation mechanism, PSOHMM can reflect the behavior changes of sellers more quickly in e-commerce systems.