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 |
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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 | |
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 |
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Country/Territory | China |
City | Beijing |
Period | 17/08/14 → 20/08/14 |
Keywords
- trust antecedents framework
- Trust prediction
- user interactions
- user ratings