Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
ICIS '00 Proceedings of the twenty first international conference on Information systems
A reputation-based approach for choosing reliable resources in peer-to-peer networks
Proceedings of the 9th ACM conference on Computer and communications security
Neural Network-Based Reputation Model in a Distributed System
CEC '04 Proceedings of the IEEE International Conference on E-Commerce Technology
An Adaptive Recommendation Trust Model in Multiagent System
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper presents a novel context-based approach to find reliable recommendations for trust model in ubiquitous environments. Context is used in our approach to analyze the user's activity, state and intention. Incremental learning based neural network is used to dispose the context in order to detect doubtful recommendations. This approach has distinct advantages when dealing with randomly given irresponsible recommendations, individual unfair recommendations as well as unfair recommendations flooding regardless of from recommenders who always give malicious recommendations or “inside job” (recommenders who acted honest previous suddenly give unfair recommendations), which is lack of consideration in the previous works. The incremental learning based neural network used in our approach also enables to filter out the unfair recommendations with limited information about the recommenders. Our simulation results show that our approach can effectively find reliable recommendations in different scenarios and a comparison is also given between previous works and our method.