C-DeepTrust: a context-aware deep trust prediction model in online social networks

Qi Wang, Weiliang Zhao, Jian Yang, Jia Wu, Shan Xue, Qianli Xing, Philip S. Yu

Research output: Contribution to journalArticlepeer-review

Abstract

Trust prediction provides valuable support for decision making, information dissemination, and product promotion in online social networks. As a complex concept in the social network community, trust relationships among people can be established virtually based on: 1) their interaction behaviors, e.g., the ratings and comments that they provided; 2) the contextual information associated with their interactions, e.g., location and culture; and 3) the relative temporal features of interactions and the time periods when the trust relationships hold. Most of the existing works only focus on some aspects of trust, and there is not a comprehensive study of user trust development that considers and incorporates 1)-3) in trust prediction. In this article, we propose a context-aware deep trust prediction model C-DeepTrust to fill this gap. First, we conduct user feature modeling to obtain the user's static and dynamic preference features in each context. Static user preference features are obtained from all the ratings and reviews that a user provided, while dynamic user preference features are obtained from the items rated/reviewed by the user in time series. The obtained context-aware user features are then combined and fed into the multilayer projection structure to further mine the context-aware latent features. Finally, the context-aware trust relationships between users are calculated by their context-aware feature vector cosine similarities according to the social homophily theory, which shows a pervasive property of social networks that trust relationships are more likely to be developed among similar people. Extensive experiments conducted on two real-world datasets show the superior performance of our approach compared with the representative baseline methods.

Original languageEnglish
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Early online date22 Sep 2021
DOIs
Publication statusE-pub ahead of print - 22 Sep 2021

Bibliographical note

Publisher Copyright:
IEEE

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Analytical models
  • Context modeling
  • Context-aware trust prediction
  • Deep learning
  • Hidden Markov models
  • Nonhomogeneous media
  • online social networks
  • Predictive models
  • Social networking (online)
  • user feature engineering.

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