Predict Pairwise Trust based on Machine Learning in Online Social Networks: A Survey

Shushu Liu, Lifang Zhang, Zheng Yan

Research output: Contribution to journalReview Articlepeer-review

15 Citations (Scopus)
110 Downloads (Pure)

Abstract

Trust plays a crucial role in online social networks where users do not communicate or interact with each other in a direct face-to-face manner. Although many researchers have already conducted comprehensive studies on trust computing like trust evaluation, pairwise trust prediction is still relatively under explored especially with machine learning methods which can overcome the disadvantages of both linear predication and trust propagation. This survey aims to fill this gap and first provides an overview of state-of-the-art researches in pairwise trust prediction using machine learning techniques, especially in the context of social networking. Specifically, we present a workflow of trust prediction using machine learning and summarize current available trust-related datasets, classifiers and different metrics used to evaluate a trained classifier. Also, we review, compare, and contrast the literature for the purpose of identifying open issues and directing future research.

Original languageEnglish
Pages (from-to)51297-51318
JournalIEEE Access
Volume6
DOIs
Publication statusPublished - 10 Sep 2018
MoE publication typeA2 Review article in a scientific journal

Keywords

  • machine learning
  • social networks
  • trust evaluation
  • Trust prediction

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