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Reliable Potential Friends Identification Based on Trust Circuit for Social Recommendation

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Book cover Wireless Algorithms, Systems, and Applications (WASA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12384))

Abstract

Direct trust links among users may be unreliable due to noise. Simple use of these direct trust links may lead to inferior recommend effects, and most of the existed methods don’t consider the difference in trust strength. We propose a novel model called TrustE which combines the trust relationships and users similarity. Specifically, we design a new method called Trust Circuit in TrustE to model trust relationships which calculates trust values by taking into account the asymmetry, transitivity, attenuation, and multiplicity-paths of trusts. Then we calculate user similarity through meta-paths guided embedded representation learning in the heterogeneous information network. Finally, we combine trust value and users similarity to get the personalized numbers of reliable potential friends for each user and make recommendation for target user according to his friends’ preferences. The experimental results on Epinions and Douban datasets verify that TrustE is superior to other existing recommendation methods and it also has high accuracy for cold-start users’ recommendation.

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Authors and Affiliations

  1. Heilongjiang University, Harbin, 150080, Heilongjiang, China

    Shuo Zhang & Jinghua Zhu

Authors
  1. Shuo Zhang
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  2. Jinghua Zhu
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Correspondence to Jinghua Zhu .

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Editors and Affiliations

  1. Shandong University, Qingdao, China

    Dongxiao Yu

  2. TU Berlin, Berlin, Germany

    Falko Dressler

  3. Qilu University of Technology, Jinan, China

    Jiguo Yu

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Zhang, S., Zhu, J. (2020). Reliable Potential Friends Identification Based on Trust Circuit for Social Recommendation. In: Yu, D., Dressler, F., Yu, J. (eds) Wireless Algorithms, Systems, and Applications. WASA 2020. Lecture Notes in Computer Science(), vol 12384. Springer, Cham. https://doi.org/10.1007/978-3-030-59016-1_59

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  • DOI: https://doi.org/10.1007/978-3-030-59016-1_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59015-4

  • Online ISBN: 978-3-030-59016-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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