Dynamic Trust Models between Users over Social Networks

Abstract

In this project, by focusing on a number of word-of-mouth communication websites, we attempted to construct dynamic trust models between users that enable to explain trust formation and its evolution processes over social networks with reasonable accuracy. Consequently, we obtained our research results on four new approaches, 1) to rank items based on the MTDF (Multinomial with Trust Discount Factor) model, 2) to estimate the conformity of users from the observed review scores, 3) to predict evolution of trust links under the presence of mediators, and 4) to analyze activities among users based on a non-negative matrix factorization (NMF) method. By using the datasets of item review scores and trust networks collected from a number of websites such as Epinions, we confirmed that our proposed models and methods can be useful basic techniques for uncovering fundamental mechanisms of trust formation and its evolution processes over trust networks. During our research period, we presented 3 papers published in peer-reviewed journals and 13 papers published in peer-reviewed conference proceedings as attached in this report.

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Document Details

Document Type
Technical Report
Publication Date
Mar 30, 2016
Accession Number
ADA636879

Entities

People

  • Kazumi Saitō

Organizations

  • University of Shizuoka

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force Research Laboratories
  • Artificial Intelligence
  • Computer Science
  • Conformity
  • Data Mining
  • Data Science
  • Data Sets
  • Information Science
  • Machine Learning
  • Media
  • Network Science
  • Personal Information Managers
  • Probability
  • Social Computing
  • Social Media
  • Social Networks

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Cybersecurity.
  • Theoretical Analysis.