TREPPS: A Trust-based Recommender System for Peer Production Services

Yung-Ming Li*, Chien Pang Kao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


Peer production, a new mode of production, is gradually shifting the traditional, capital-intensive wealth production to a model which heavily depends on information creating and sharing. More and more online users are relying on this type of services such as news, articles, bookmarks, and various user-generated contents around World Wide Web. However, the quality and the veracity of peers' contributions are not well managed. Without a practical means to assess the quality of peer production services, the consequence is information-overloading. In this study, we present a recommender system based on the trust of social networks. Through the trust computing, the quality and the veracity of peer production services can be appropriately assessed. Two prominent fuzzy logic applications - fuzzy inference system and fuzzy MCDM method are utilized to support the decision of service choice. The experimental results showed that the proposed recommender system can significantly enhance the quality of peer production services and furthermore overcome the information overload problems. In addition, a trust-based social news system is built to demonstrate the application of the proposed system.

Original languageEnglish
Pages (from-to)3263-3277
Number of pages15
JournalExpert Systems with Applications
Issue number2 PART 2
StatePublished - 1 Jan 2009


  • Fuzzy inference systems
  • Peer production
  • Recommender systems
  • Trust computing

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