Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Extraction of a latent blog community based on subject
Proceedings of the 18th ACM conference on Information and knowledge management
Credibility: A multidisciplinary framework
Annual Review of Information Science and Technology
iCLUB: an integrated clustering-based approach to improve the robustness of reputation systems
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Subject-based extraction of a latent blog community
Information Sciences: an International Journal
Outlier detection using centrality and center-proximity
Proceedings of the 21st ACM international conference on Information and knowledge management
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The average of the customer ratings on the product, which we call reputation, is one of the key factors in online purchasing decision of a product. There is, however, no guarantee in the trustworthiness of the reputation since it can be manipulated rather easily. In this paper, we define false reputation as the problem of the reputation to be manipulated by unfair ratings, and design a general framework that provides trustable reputation. For this purpose, we propose TRUEREPUTATION, an algorithm that iteratively adjusts the reputation based on the confidence of customer ratings.