A High-Reliability Multi-Faceted Reputation Evaluation Mechanism for Online Services

Miao Wang, Guiling Wang, Yujun Zhang, Zhongcheng Li

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


In today's society, there are plenty of services available, and customers are facing bigger challenge in choosing them than ever before. Therefore, it is important to build a reliable reputation mechanism for selecting a credible service. To address the challenges of reputation evaluation, including the diverse and dynamic natures of services, incompleteness of user feedback, and intricacy of malicious ratings, a High-reliability Multi-faceted Reputation evaluation mechanism for online services (HMRep) is proposed. First, HMRep starts with addressing the incomplete feedback and estimates missing ratings based on both the service quality and a user's rating behavior. Second, HMRep identifies and removes malicious collusive raters and irresponsible raters to improve the accuracy of reputation calculation. Further, the reputation calculation is based on the user credibility and incorporates historical information to reflect the change of the services. Finally, we provide a multi-faceted evaluation method to satisfy some specific needs of customers who are only concerned about a subset of a services features. Experimental results verify the design of HMRep, and reveal HMRep can effectively defend against malicious ratings, and accurately calculate the reputation values of services. HMRep can be applied in lots of sectors for different kinds of services, especially those complex ones.

Original languageEnglish (US)
Article number7782457
Pages (from-to)836-850
Number of pages15
JournalIEEE Transactions on Services Computing
Issue number6
StatePublished - Nov 1 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management


  • Reputation evaluation
  • incomplete user feedback
  • index weights
  • malicious ratings


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