Abstract

Trust plays a crucial role in helping online users collect reliable information and it has gained increasing attention from the computer science community in recent years. Traditionally, research about online trust assumes static trust relations between users. However, trust, as a social concept, evolves as people interact. Most existing studies about trust evolution are from sociologists in the physical world while little work exists in an online world. Studying online trust evolution faces unique challenges because more often than not, available data is from passive observation. In this work, we leverage social science theories to develop a methodology that enables the study of online trust evolution. In particular, we identify the differences of trust evolution study in physical and online worlds and propose a framework, eTrust, to study trust evolution using online data from passive observation in the context of product review sites by exploiting the dynamics of user preferences. We present technical details about modeling trust evolution, and perform experiments to show how the exploitation of trust evolution can help improve the performance of online applications such as trust prediction, rating prediction and ranking evolution.

Original languageEnglish (US)
Article number6990611
Pages (from-to)1724-1738
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume27
Issue number6
DOIs
StatePublished - Jun 1 2015

Keywords

  • Multi-faceted Trust
  • Preference-based Trust Evolution
  • Social Recommendation
  • Trust Prediction
  • User Preference

ASJC Scopus subject areas

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

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