Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
A Computational Model of Trust and Reputation for E-businesses
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
Collaborative Reputation Mechanisms in Electronic Marketplaces
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
The capacity of low-density parity-check codes under message-passing decoding
IEEE Transactions on Information Theory
Results on Punctured Low-Density Parity-Check Codes and Improved Iterative Decoding Techniques
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
A belief propagation based recommender system for online services
Proceedings of the fourth ACM conference on Recommender systems
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Trust and reputation play critical roles in most environments wherein entities participate in various transactions and protocols among each other. The recipient of the service has no choice but to rely on the reputation of the service provider based on the latter's prior performance. This paper introduces an iterative method for trust and reputation management referred as ITRM. The proposed algorithm can be applied to centralized schemes, in which a central authority collects the reports and forms the reputations of the service providers as well as report/rating trustworthiness of the (service) consumers. The proposed iterative algorithm is inspired by the iterative decoding of low-density parity-check codes over bipartite graphs. The scheme is robust in filtering out the peers who provide unreliable ratings. We provide a detailed evaluation of ITRM via analysis and computer simulations. Further, comparison of ITRM with some well-known reputation management techniques (e.g., Averaging Scheme, Bayesian Approach and Cluster Filtering) indicates the superiority of our scheme both in terms of robustness against attacks (e.g., ballot-stuffing, bad-mouthing) and efficiency. Furthermore, we show that the computational complexity of the proposed ITRM is far less than the Cluster Filtering; which has the closest performance (to ITRM) in terms of resiliency to attacks. Specifically, the complexity of ITRM is linear in the number of clients, while that of the Cluster Filtering is quadratic.