@inproceedings{4fcdcf926839477489f1363f0f4e9af4,
title = "Bayesian memory-based reputation system",
abstract = "Reputation System provides a way to maintain trust through social control by utilizing feedbacks about the service providers' past behaviors. Conventional Memory-based Reputation System (MRS) is one of the most successful mechanisms in terms of accuracy. Though MRS performs well on giving predicted values for service providers offering averaging quality services, our experiments show that MRS performs poor on giving predicted values for service providers offering high and low quality services. We propose a Bayesian Memory-based Reputation System (BMRS) which uses Bayesian Theory to analyze the probability distribution of the predicted valued given by MRS and makes suitable adjustment. The simulation results, which are based on EachMovie dataset, show that our proposed BMRS has higher accuracy than MRS on giving predicted values for service providers offering high and low quality services.",
keywords = "Bayesian Theory, Memory-based, Reputation System",
author = "Weiwei Yuan and Donghai Guan and Sungyoung Lee and Lee, {Young Koo} and Heejo Lee",
note = "Funding Information: This research was supported by the MIC (Ministry of Information and Communication), Korea, Under the ITFSIP (IT Foreign Specialist Inviting Program) supervised by the IITA (Institute of Information Technology Advancement). Publisher Copyright: Copyright 2007 ICST.; 3rd International Conference on Mobile Multimedia Communications, MobiMedia 2007 ; Conference date: 27-08-2007 Through 29-08-2007",
year = "2007",
month = aug,
day = "27",
doi = "10.4108/icst.mobimedia2007.1809",
language = "English",
series = "MobiMedia 2007 - Proceedings of the 3rd International Conference on Mobile Multimedia Communications",
publisher = "Association for Computing Machinery, Inc",
booktitle = "MobiMedia 2007 - Proceedings of the 3rd International Conference on Mobile Multimedia Communications",
}