Multi-aspect + Transitivity + Bias: An integral trust inference model

Yuan Yao, Hanghang Tong, Xifeng Yan, Feng Xu, Jian Lu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Inferring the pair-wise trust relationship is a core building block for many real applications. State-of-the-art approaches for such trust inference mainly employ the transitivity property of trust by propagating trust along connected users, but largely ignore other important properties such as trust bias, multi-aspect, etc. In this paper, we propose a new trust inference model to integrate all these important properties. To apply the model to both binary and continuous inference scenarios, we further propose a family of effective and efficient algorithms. Extensive experimental evaluations on real data sets show that our method achieves significant improvement over several existing benchmark approaches, for both quantifying numerical trustworthiness scores and predicting binary trust/distrust signs. In addition, it enjoys linear scalability in both time and space.

Original languageEnglish (US)
Article number6585254
Pages (from-to)1706-1719
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number7
StatePublished - Jul 2014


  • Trust inference
  • latent factors
  • multi-aspect property
  • transitivity property
  • trust bias
  • trust prediction

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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