Paper Info
Title
Implicit User Trust Modeling Based On User Attributes And Behavior In Online Social Networks
Abstract
In this paper, we present a new user trustworthiness estimation model for social networks (SN), whereas most of existing researches have been focused on the user-user/item relationship trustworthiness estimation. Users share information of their interest on various social media without their trustworthiness verification. Therefore, SN are susceptible to malicious users for misinformation spreading. In SN, the original information source is generally unknown and the user who is sharing the contents is the only known information about the source. Therefore, the users trustworthiness is an effective criterion for SN contents trustworthiness estimation. However, the users are unable to identify trustworthy/untrustworthy users, and the existing user-user/item relationship models do not provide user trustworthiness information. Our proposed model provides a systematic way to assess the user trustworthiness based on user attributes and interaction behavior. The proposed model is helpful to avoid the trust sparsity (implicit trust model), trust subjectivity (users objective/collective trustworthiness estimation model) and cold-start users trustworthiness (users attributes-based trust modeling) problems. We employ friends-recommendation (FR) as an exemplary application to evaluate the performance of our proposed model in trust-aware recommendations. Simulation results illustrate that our trust-aware FR model outperformed the existing trust and FR models.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2943877
IEEE ACCESS
Keywords
DocType
Volume
Computational modeling, Estimation, Social networking (online), Reliability, Sociology, Psychology, Systematics, Credibility, reliability, social-ties strength, trust-aware recommendation, user trustworthiness modeling
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Jebran Khan100.34
Sungchang Lee200.34