ETAF

An extended trust antecedents framework for trust prediction

Conference paper (2014)

Authors

Guibing Guo Nanyang Technological University
Jie Zhang Nanyang Technological University
Daniel Thalmann Nanyang Technological University
N. Yorke-Smith American University of Beirut
Affiliation
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Published Date

10-10-2014

Language

English

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Abstract

Trust is one source of information that has been widely adopted to personalize online services for users, such as in product recommendations. However, trust information is usually very sparse or unavailable for most online systems. To narrow this gap, we propose a principled approach that predicts implicit trust from users' interactions, by extending a well-known trust antecedents framework. Specifically, we consider both local and global trustworthiness of target users, and form a personalized trust metric by further taking into account the active user's propensity to trust. Experimental results on two real-world datasets show that our approach works better than contemporary counterparts in terms of trust ranking performance when direct user interactions are limited.