Characterizing the reputation of evaluators using vectors in the object feature space. (1st September 2022)

• Record Type: Journal Article
• Authors: Li, Meng; Jiang, Yuanxiang; Di, Zengru
• Is Part Of: Expert systems with applications. Volume 201(2022)
• Publication Date: 2022-09-01
• Subjects: Online rating system -- Reputation evaluation -- Object classification -- Clustering; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117136
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Evaluation is a frequent occurrence in our daily lives, especially in online systems. Establishing ways to characterize the reputation of an evaluator is therefore becoming an important problem in online systems, and this has attracted much attention. Most existing evaluation methods use the rating information in user–object bipartite networks to evaluate the reputations of evaluators. These methods use a scalar quantity to represent the reputation of each user, which is applied to try to identify malicious ratings and spamming attacks. In this paper, we suggest that the reputation should be characterized by a vector, and propose a reputation–evaluation algorithm based on the classification of objects. More specifically, the objects are classified into several categories according to the community structure information obtained from a one-mode projection onto the objects of a bipartite network. The reputation vector of an evaluator then represents his/her reputation for the objects in different categories. The evaluator is also assigned an appropriate attribute according to his/her reputation vector using K-means clustering. The results from both artificial and real rating data have shown that the presented method is better than a correlation-based ranking method in terms of both accuracy and robustness. Highlights: Suggested to use a vector to characterize the reputation of the evaluators. Introduced a method to find the evaluator's reputation vector and object's rate. The community structure of the objects is taken as the feature space of the objects. The results show the group structure of the evaluators. Performance of the method better than the original correlation-based algorithm.