A Survey on Trust Prediction in Online Social Networks

被引:23
|
作者
Ghafari, Seyed Mohssen [1 ]
Beheshti, Amin [1 ]
Joshi, Aditya [2 ]
Paris, Cecile [2 ]
Mahmood, Adnan [1 ]
Yakhchi, Shahpar [1 ]
Orgun, Mehmet A. [1 ]
机构
[1] Macquarie Univ, Dept Comp, Sydney, NSW 2109, Australia
[2] CSIRO Data61, Marsfield, NSW 2122, Australia
来源
IEEE ACCESS | 2020年 / 8卷
关键词
Social network services; Australia; Psychology; Computer science; Recommender systems; Business; Sociology; Context-aware; data sparsity problem; online social networks; pair-wise trust prediction; trust; trust relations; time-aware; RECOMMENDATION METHOD; CONTEXT; REPUTATION; MANAGEMENT; DYNAMICS;
D O I
10.1109/ACCESS.2020.3009445
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Level of Trust can determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for measuring trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this paper, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs, classify them based on different factors, and propose some future directions for researchers interested in this field.
引用
收藏
页码:144292 / 144309
页数:18
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