A survey of trust and reputation systems for online service provision
Decision Support Systems
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
Applied Intelligence
A trust-based noise injection strategy for privacy protection in cloud
Software—Practice & Experience
TRUSTCOM '12 Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications
More reputable recommenders give more accurate recommendations?
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Exploring Trust-Based Service Value Chain Framework in Tele-healthcare Services
HICSS '13 Proceedings of the 2013 46th Hawaii International Conference on System Sciences
| Hi-index | 0.00 |
Some trust-aware applications only involve the information provided by reputable users to ensure the reliability. A common way to extract the reputable users is to set a threshold value for the user reputation, and regard those whose reputations bigger than this value as the reputable users. However, existing works just intuitively set the threshold value for the reputable user extraction and did not consider the ratio of available information by setting this value. In this work, we analyze the distribution of the user reputation, and give clear criteria on setting the threshold value for reputable user extraction. Furthermore, we analyze 17 trust network datasets extracted from the real applications to give a general guidance on setting the reputation threshold value. We suggest that for the normalized reputation varying from 0 to 1, the threshold value for the reputable user extraction should be no more than 0.2 to ensure around 10% information available for use.