Affiliations: [a] School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia. E-mails: [email protected], [email protected], [email protected]
| [b] Department of Informatics, University of Oslo, Oslo, Norway. E-mail: [email protected]
Abstract: With the extensive use of rating systems in the web, and their significance in decision making process by users, the need for more accurate aggregation methods has emerged. The Naïve aggregation method, using the simple mean, is not adequate anymore in providing accurate reputation scores for items [6], hence, several researches where conducted in order to provide more accurate alternative aggregation methods. Most of the current reputation models do not consider the distribution of ratings across the different possible ratings values. In this paper, we propose a novel reputation model, which generates more accurate reputation scores for items by deploying the normal distribution over ratings. Experiments show promising results for our proposed model over state-of-the-art ones on sparse and dense datasets.
