Identifying Spammers by Completing the Ratings of Low-Degree Users

Guo Hua Li, Jun Wu, Hong Liang Sun

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


Along with the rapid development of e-commerce, a large number of spammers disrupt the fair order of the e-commerce platform. The false ratings rated by these spammers do not match the quality of items, confusing the boundaries of good and bad items and seriously endangering the real interests of merchants and normal users. To eliminate the malicious influence caused by these spammers, many effective spamming detection algorithms are proposed in e-commerce platforms. However, these algorithms are ineffective in judging how trustworthy a user with insufficient rating data. In order to address this issue, we take inspiration from traditional recommender systems by completing the missing ratings of low-degree users to improve the efficiency of spamming detection algorithms when approaching those users. User similarity is used in this paper to predict the missing ratings of users. A novel reputation ranking method is proposed. We then test our improvements compared with DR, IGR, and IOR. Experimental results on three typical data sets suggest that our method combined with IOR has improved by at least (formula presented) in dealing with malicious spammers, respectively. As for results on detecting random spammers, our method improves by a least (formula presented), respectively.

Original languageEnglish
Title of host publicationBig Data and Social Computing - 7th China National Conference, BDSC 2022, Revised Selected Papers
EditorsXiaofeng Meng, Qi Xuan, Yang Yang, Yang Yue, Zi-Ke Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9789811975318
Publication statusPublished - 2022
Event7th China National Conference on Big Data and Social Computing, BDSC 2022 - Hangzhou, China
Duration: 11 Aug 202213 Aug 2022

Publication series

NameCommunications in Computer and Information Science
Volume1640 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference7th China National Conference on Big Data and Social Computing, BDSC 2022


  • E-commerce
  • Fraud detection
  • Rating prediction
  • Spamming attacks
  • User similarity

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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