Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2008

Abstract

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.

Keywords

trust prediction, user interaction, online community

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

EC '08: Proceedings of the 9th ACM conference on Electronic Commerce: Chicago, July 8-12

First Page

310

Last Page

319

ISBN

9781605581699

Identifier

10.1145/1386790.1386838

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/1386790.1386838

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