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

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.

Filtering Compromised Environment Sensors Using Autoregressive Hidden Markov Model

“…HMMs have been successfully used to filter unreliable agents, see Chang & Jiliu [7], Anjum et al [8]. However the approach is unable to model correlation between observations.…”

Security analysis of online digital goods business based on stochastic game net model

“…Chang and Ouzrout et al . presented a reputation evaluation mechanism based on the optimized hidden Markov model to describe the dynamic behavior of sellers in digital goods business . Zhu et al .…”

“…Jiang et al presented a general Bayesian Stackelberg game model for security patrolling in dynamic uncertain domains, in which the uncertainty in the execution of patrols is represented using Markov decision processes [7]. Chang and Ouzrout et al presented a reputation evaluation mechanism based on the optimized hidden Markov model to describe the dynamic behavior of sellers in digital goods business [8]. Zhu et al presented application of game theory in different forms of security and privacy risks in networks.…”

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“…With the rapid development of network technology, peerto-peer networks have been widely applied in a variety of distributed application systems, such as P2P file sharing, electronic commerce [1], and cloud computing, which make Internet applications simpler and more efficient. However, to guarantee the security and credibility of these network application environments is very difficult due to the openness, anonymity, and high dynamic.…”