Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2009

Abstract

Trust between users is an important piece of knowledge that can be exploited in search and recommendation.Given that user-supplied trust relationships are usually very sparse, we study the prediction of trust relationships using user interaction features in an online user generated review application context. We show that trust relationship prediction can achieve better accuracy when one adopts personalized and cluster-based classification methods. The former trains one classifier for each user using user-specific training data. The cluster-based method first constructs user clusters before training one classifier for each user cluster. Our proposed methods have been evaluated in a series of experiments using two datasets from Epinions.com. It is shown that the personalized and cluster-based classification methods outperform the global classification method, particularly for the active users.

Keywords

Trust prediction, Web of trust

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

ACM CIKM Workshop on Complex Networks Meet Information & Knowledge Management (CNIKM)

First Page

47

Last Page

54

ISBN

9781605588070

Identifier

10.1145/1651274.1651284

Publisher

ACM

City or Country

Hong Kong

Additional URL

http://doi.org/10.1145/1651274.1651284

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