Analysis of ratings on trust inference in open environments

Ratings (also known as recommendations) provide an efficient and effective way to build trust relationship in the human society, by making use of the information from others rather than exclusively relying on one's own direct observations. However, it is uncertain that whether the rating can play the same positive effect in the open computing environment because of differences between the computing world and human society. We envisage that there are two kinds of uncertainties: the uncertainty resulting from rating aggregation algorithms and the uncertainty resulting from other algorithm-independent design factors, which are coined as algorithm uncertainty and factor uncertainty in this paper. The algorithm uncertainty is related to such a problem: are the complex aggregating algorithms necessary? The factor uncertainty refers to how the performance of ratings is affected by all kinds of factors, including trust model design related factors and trust model design independent facto...

• Type: Journal
• Year: 2008
• Where: PE
• Authors: Zhengqiang Liang, Weisong Shi
• Keywords: Algorithm, Algorithm Uncertainty, Open Computing Environments, Optimization, PE 2008