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Towards time-aware context-aware deep trust prediction in online social networks

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posted on 28.03.2022, 15:50 authored by Seyed Mohssen Ghafari
Trust can be defined as a measure to 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 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 dissertation, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs. We discuss three main challenges in this domain and present novel trust prediction approaches to address them. We first focus on proposing a low-rank representation of users that incorporates users' personality traits as additional information. Then, we propose a set of context-aware trust prediction models. Finally, by considering the time-dependency of trust relations, we propose a dynamic deep trust prediction approach. We design and implement five pair-wise trust prediction approaches and evaluate them with real-world datasets collected from OSNs. The experimental results demonstrate the effectiveness of our approaches compared to other state-of-the-art pair-wise trust prediction models.


Table of Contents

1. Introduction -- 2. Related work -- 3. Experimental setup -- 4. Modelling personality traits in trust prediction -- 5. Proposing context-aware trust prediction approaches -- 6. A dynamic deep trust prediction approach for online social networks -- 7. Conclusions and future work -- References.


Theoretical thesis. Bibliography: pages 87-102

Awarding Institution

Macquarie University

Degree Type

Thesis PhD


PhD, Macquarie University, Faculty of Science and Engineering, Department of Computing

Department, Centre or School

Department of Computing

Year of Award


Principal Supervisor

Amin Beheshti

Additional Supervisor 1

Aditya Joshi


Copyright Seyed Mohssen Ghafari 2019. Copyright disclaimer: http://mq.edu.au/library/copyright




1 online resource (xxii, 102 pages): illustrations

Former Identifiers

mq:71680 http://hdl.handle.net/1959.14/1276987