DeepTrust

a deep user model of homophily effect for trust prediction

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Trust prediction in online social networks is crucial for information dissemination, product promotion, and decision making. Existing work on trust prediction mainly utilizes the network structure or the low-rank approximation of a trust network. These approaches can suffer from the problem of data sparsity and prediction accuracy. Inspired by the homophily theory, which shows a pervasive feature of social and economic networks that trust relations tend to be developed among similar people, we propose a novel deep user model for trust prediction based on user similarity measurement. It is a comprehensive data sparsity insensitive model that combines a user review behavior and the item characteristics that this user is interested in. With this user model, we firstly generate a user's latent features mined from user review behavior and the item properties that the user cares. Then we develop a pair-wise deep neural network to further learn and represent these user features. Finally, we measure the trust relations between a pair of people by calculating the user feature vector cosine similarity. Extensive experiments are conducted on two real-world datasets, which demonstrate the superior performance of the proposed approach over the representative baseline works.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages618-627
Number of pages10
ISBN (Electronic)9781728146034
ISBN (Print)9781728146058
DOIs
Publication statusPublished - 1 Nov 2019
Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
Duration: 8 Nov 201911 Nov 2019

Conference

Conference19th IEEE International Conference on Data Mining, ICDM 2019
CountryChina
CityBeijing
Period8/11/1911/11/19

Keywords

  • Online social networks
  • Trust prediction
  • User modeling

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  • Cite this

    Wang, Q., Zhao, W., Yang, J., Wu, J., Hu, W., & Xing, Q. (2019). DeepTrust: a deep user model of homophily effect for trust prediction. In J. Wang, K. Shim, & X. Wu (Eds.), Proceedings - 19th IEEE International Conference on Data Mining, ICDM 2019 (pp. 618-627). [8970846] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICDM.2019.00072