The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
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
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Proceedings of the 10th international conference on Intelligent user interfaces
WWW '05 Proceedings of the 14th international conference on World Wide Web
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Analysis of a low-dimensional linear model under recommendation attacks
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Jiminy: a scalable incentive-based architecture for improving rating quality
iTrust'06 Proceedings of the 4th international conference on Trust Management
On trust models and trust evaluation metrics for ad hoc networks
IEEE Journal on Selected Areas in Communications
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
A unified framework for reputation estimation in online rating systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present a class of voting systems that we call “iterative filtering” systems. These systems are based on an iterative method that assigns a reputation to $n+m$ items, $n$ objects, and $m$ raters, applying some filter to the votes. Each rater evaluates a subset of objects leading to an $n\times m$ rating matrix with a given sparsity pattern. From this rating matrix a formula is defined for the reputation of raters and objects. We propose a natural and intuitive nonlinear formula and also provide an iterative algorithm that linearly converges to the unique vector of reputations. In contrast to classical outlier detection, no evaluation is discarded in this method, but each one is taken into account with different weights for the reputations of the objects. The complexity of one iteration step is linear in the number of evaluations, making our algorithm efficient for large data sets. Experiments show good robustness of the reputation of the objects against cheaters and spammers and good detection properties of cheaters and spammers.