ETAF: An extended trust antecedents framework for trust prediction

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

doi:10.1109/ASONAM.2014.6921639

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 volume › Conference contribution › Scientific › peer-review

119 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 language	English
Title of host publication	ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Publisher	IEEE
Pages	540-547
Number of pages	8
ISBN (Electronic)	9781479958771
DOIs	https://doi.org/10.1109/ASONAM.2014.6921639
Publication status	Published - 10 Oct 2014
Externally published	Yes
Event	2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 - Beijing, China Duration: 17 Aug 2014 → 20 Aug 2014

Conference

Conference	2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
Country/Territory	China
City	Beijing
Period	17/08/14 → 20/08/14

Keywords

trust antecedents framework
Trust prediction
user interactions
user ratings

Access to Document

10.1109/ASONAM.2014.6921639

Cite this

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title = "ETAF: An extended trust antecedents framework for trust prediction",

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.",

keywords = "trust antecedents framework, Trust prediction, user interactions, user ratings",

author = "Guibing Guo and Jie Zhang and Daniel Thalmann and Neil Yorke-Smith",

year = "2014",

month = oct,

day = "10",

doi = "10.1109/ASONAM.2014.6921639",

language = "English",

pages = "540--547",

booktitle = "ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining",

publisher = "IEEE",

address = "United States",

note = "2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 ; Conference date: 17-08-2014 Through 20-08-2014",

}

Guo, G, Zhang, J, Thalmann, D & Yorke-Smith, N 2014, ETAF: An extended trust antecedents framework for trust prediction. in ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining., 6921639, IEEE, pp. 540-547, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014, Beijing, China, 17/08/14. https://doi.org/10.1109/ASONAM.2014.6921639

ETAF: An extended trust antecedents framework for trust prediction. / Guo, Guibing; Zhang, Jie; Thalmann, Daniel et al.
ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 2014. p. 540-547 6921639.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - ETAF

T2 - 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014

AU - Guo, Guibing

AU - Zhang, Jie

AU - Thalmann, Daniel

AU - Yorke-Smith, Neil

PY - 2014/10/10

Y1 - 2014/10/10

N2 - 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.

AB - 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.

KW - trust antecedents framework

KW - Trust prediction

KW - user interactions

KW - user ratings

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M3 - Conference contribution

AN - SCOPUS:84911212529

SP - 540

EP - 547

BT - ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

PB - IEEE

Y2 - 17 August 2014 through 20 August 2014

ER -

ETAF: An extended trust antecedents framework for trust prediction

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Keywords

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