Title :
A reputation inference model based on linear hidden markov process
Author :
Wang, Xiaofeng ; Ou, Wei ; Su, Jinshu
Author_Institution :
Sch. of Comput. Sci., Nat. Univ. of Defence Technol., Changsha, China
Abstract :
Although reputation is a statistical value about the trust, most existing work uses the summation method for reputation aggregation, which is vulnerable to malicious feedbacks and cannot assess the reputation prediction variance. In this paper, we present a novel Linear Hidden Markov (LHM) model for reputation evaluation. LHM model uses the linear autoregressive function to define the reputation evolution, so that the reputation prediction variance can be assessed by a Markov process. Based on Expectation Maximization (EM) calibration method, LHM model aggregates the feedback by using the Kalman filter, which can support further robust inference techniques. Our experiments show that LHM model can effectively capture the reputation value and its prediction variance.
Keywords :
Kalman filters; expectation-maximisation algorithm; hidden Markov models; inference mechanisms; statistics; Kalman filter; expectation maximization calibration; linear hidden Markov process; reputation inference model; robust inference techniques; statistical value; Aggregates; Bayesian methods; Calibration; Communication system control; Hidden Markov models; Kalman filters; Linear feedback control systems; Predictive models; Robustness; Technology management; Expectation Maximization; Kalman Filter; prediction variance; reputation;
Conference_Titel :
Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on
Conference_Location :
Sanya
Print_ISBN :
978-1-4244-4247-8
DOI :
10.1109/CCCM.2009.5270424