  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.
