Evaluating user reputation of online rating systems by rating statistical patterns

Hong Liang Sun, Kai Ping Liang, Hao Liao, Duan Bing Chen

Research output: Journal PublicationArticlepeer-review

7 Citations (Scopus)

Abstract

Numerous complex systems such as rating systems are highly affected by a small number of spamming attackers. How to design a fast and effective ranking method under the threat of spamming attacks is significant in practice. In this paper, we extract the user's rating characteristics from personal historical ratings to determine whether the user is normal. It is discovered that reliable users have little bias and their rating scores follow the pattern of peak distribution. On the opposite, malicious users usually have biased ratings and their rating scores scarcely follow a known pattern. A new reputation ranking method IOR (Iterative Optimization Ranking) is proposed based on user rating deviation and rating characteristics. The experimental results on three real datasets show that this method is more efficient than existing states of art methods. This new fundamental idea can be contributed to a new way to solve spammer attacking problem. It can also be applied in large and sparse bipartite rating networks in a short time.

Original languageEnglish
Article number106895
JournalKnowledge-Based Systems
Volume219
DOIs
Publication statusPublished - 11 May 2021

Keywords

  • Complex networks
  • Iterative refinement
  • Rating systems
  • Spamming attacks

ASJC Scopus subject areas

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence

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