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Trust can be defined as a measure to determine which source of information isreliable and with whom we should share or from whom we should acceptinformation. There are several applications for trust in Online Social Networks(OSNs), including social spammer detection, fake news detection, retweetbehaviour detection and recommender systems. Trust prediction is the process ofpredicting a new trust relation between two users who are not currentlyconnected. In applications of trust, trust relations among users need to bepredicted. This process faces many challenges, such as the sparsity ofuser-specified trust relations, the context-awareness of trust and changes intrust values over time. In this dissertation, we analyse the state-of-the-artin pair-wise trust prediction models in OSNs. We discuss three main challengesin this domain and present novel trust prediction approaches to address them.We first focus on proposing a low-rank representation of users thatincorporates users' personality traits as additional information. Then, wepropose a set of context-aware trust prediction models. Finally, by consideringthe time-dependency of trust relations, we propose a dynamic deep trustprediction approach. We design and implement five pair-wise trust predictionapproaches and evaluate them with real-world datasets collected from OSNs. Theexperimental results demonstrate the effectiveness of our approaches comparedto other state-of-the-art pair-wise trust prediction models.

Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks