Conference Proceeding Article
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.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
EC '08: Proceedings of the 9th ACM conference on Electronic Commerce
City or Country
LIU, Haifeng; LIM, Ee Peng; LAUW, Hady Wirawan; LE, Minh-Tam; SUN, Aixin; SRIVASTAVA, Jaideep; and KIM, YoungAe.
Predicting trusts among users of online communities: an Epinions case study. (2008). EC '08: Proceedings of the 9th ACM conference on Electronic Commerce. 310-319. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/918