ETAF: An extended trust antecedents framework for trust prediction

Guibing Guo, Jie Zhang, Daniel Thalmann, Neil Yorke-Smith

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

69 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
PublisherIEEE
Pages540-547
Number of pages8
ISBN (Electronic)9781479958771
DOIs
Publication statusPublished - 10 Oct 2014
Externally publishedYes
Event2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 - Beijing, China
Duration: 17 Aug 201420 Aug 2014

Conference

Conference2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
CountryChina
CityBeijing
Period17/08/1420/08/14

Keywords

  • trust antecedents framework
  • Trust prediction
  • user interactions
  • user ratings

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