A Robust Rating Aggregation Method based on Rater Group Trustworthiness for Collusive Disturbance

Huan Zhu, Yu Xiao, Dongmei Chen and Jun Wu ()
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Huan Zhu: Beijing Normal University
Yu Xiao: Beijing Normal University
Dongmei Chen: Beijing Normal University
Jun Wu: Beijing Normal University

Information Systems Frontiers, 2025, vol. 27, issue 3, No 7, 1011 pages

Abstract: Abstract Rating can be obligatory for many tasks, such as film recommendation, hotel rating, and product evaluation. Aggregating ratings given by numerous raters is a necessary and effective way to obtain comprehensive evaluation of the objects. While the awareness of potential distortion for some of the targeted objects, has attracted substantial attention of researchers and motivated the designing of the robust rating aggregation method to overcome the impact of disturbance from ignorant/malicious raters in practice. In this paper, we focus on rating aggregation with collusive disturbance, which is hard to be eliminated and invalidate traditional rating aggregation methods. Therefore, we will introduce the idea of detecting collusive group into rating aggregation to develop a new method, called robust rating aggregation method based on rater group trustworthiness (RGT), which obtains four main modules: Graph Mapping, Rater Group Detection, Group Trustworthiness Calculating, and Rating Aggregation. Experimental results and analyses demonstrate that our method is more robust to collusive disturbance than other traditional methods.

Keywords: Rating aggregation; Collusive Disturbance; Group Trustworthiness; Robustness (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10796-024-10489-8

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