@inproceedings{290a909eff9543a19acc1ed66ac1bf16,
title = "A Reputation Ranking Method based on Rating Patterns and Rating Deviation",
abstract = "In the e-commerce platform, user purchase behavior often depends on personal experience and the objective ratings of others. Various businesses employ a large number of spammers to obtain illegal benefits by distorting the ranking of goods, which seriously affects the market order. How to design a high- speed and effective ranking method to remove these spammers is necessary and significant. In this paper, a novel reputation ranking method is proposed based on users' Rating Patterns and Rating Deviation (RPRD) because users' rating preference and historical behavior differ significantly with spammers. We compare RPRD method with three classical methods Deviation- based Ranking (DR), Iterative Group-based Ranking (IGR) and Iterative Balance Ranking (IBR) on three real datasets. Experimental results show that the RPRD method can effectively resist spammers attack and identification, especially in detecting random spammers. On the other hand, this method always has high accuracy and robustness even if the network is relatively sparse. It can also be applied in large and sparse bipartite rating networks in a short time.",
keywords = "Fraud Detection, Rating Patterns, Spammers Attack",
author = "Jian Zhou and Liu, {Yu Feng} and Sun, {Hong Liang}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 5th International Conference on Data Science and Information Technology, DSIT 2022 ; Conference date: 22-07-2022 Through 24-07-2022",
year = "2022",
doi = "10.1109/DSIT55514.2022.9943923",
language = "English",
series = "2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings",
address = "United States",
}
