Predicting trusts among users of online communities: An epinions case study

Haifeng Liu, Ee Peng Lim, Hady W. Lauw, Minh Tam Le, Aixin Sun, Jaideep Srivastava, Young Ae Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

155 Scopus citations


Trust between a pair of users is an important piece of information for users in an online community (such as electronic commerce websites and product review websites) where users may rely on trust information to make decisions. In this paper, we address the problem of predicting whether a user trusts another user. Most prior work infers unknown trust ratings from known trust ratings. The effectiveness of this approach depends on the connectivity of the known web of trust and can be quite poor when the connectivity is very sparse which is often the case in an online community. In this paper, we therefore propose a classification approach to address the trust prediction problem. We develop a taxonomy to obtain an extensive set of relevant features derived from user attributes and user interactions in an online community. As a test case, we apply the approach to data collected from Epinions, a large product review community that supports various types of interactions as well as a web of trust that can be used for training and evaluation. Empirical results show that the trust among users can be effectively predicted using pre-trained classifiers.

Original languageEnglish (US)
Title of host publicationEC'08 - Proceedings of the 2008 ACM Conference on Electronic Commerce
Number of pages10
StatePublished - 2008
Event2008 ACM Conference on Electronic Commerce, EC'08 - Chicago, IL, United States
Duration: Jul 8 2008Jul 12 2008

Publication series

NameProceedings of the ACM Conference on Electronic Commerce


Other2008 ACM Conference on Electronic Commerce, EC'08
Country/TerritoryUnited States
CityChicago, IL


  • Online community
  • Trust prediction
  • User interaction


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