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International Conference on Web Information Systems Engineering

WISE 2015: Web Information Systems Engineering – WISE 2015 pp 185–199Cite as

Implicit Trust and Distrust Prediction for Recommender Systems

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9418))

Abstract

Many trust-aware recommender systems (TARSs) have explored the value of explicit trust which is specified by users with binary values. But existing works of TARSs suffer from the problem of the lack of explicit trust which may not always be available in online social networks. In order to solve this issue, some implicit trust based TARSs are proposed. However, these methods generally predict implicit trust scores between users based on the interpersonal aspect, i.e., propagated trust, similarity obtained by user ratings on co-rated items, while ignore the personal aspects, i.e., trust bias and the impersonal aspects, i.e., local-topology-based features in trust networks, which are also important for implicit trust prediction. In this paper, we attempt to propose a classification approach to address the trust/distrust prediction problem. First, we obtain an extensive set of relevant features derived from the personal aspects, interpersonal aspects and impersonal aspects of trust. Then a logistic regression model is developed and trained by accommodating these factors, and applied to predict continuous values of users’ trust and distrust. We conduct an empirical study to evaluate the accuracy of the predicted implicit trust. The experimental results on real-world data sets demonstrate the effectiveness of our proposed model in employing implicit trust/distrust into existing trust-aware recommendation approaches.

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Notes

  1. 1.

    www.trustlet.org.

  2. 2.

    https://github.com/quinnliu/machineLearning.

  3. 3.

    www.mymedialite.net.

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Acknowledgment

This research is funded by the National Natural Science Foundation of China under grant No. 61272406 and the Fundamental Research Funds for the Central Universities, HUST: 2013TS101.

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Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China

    Xiao Ma, Hongwei Lu & Zaobin Gan

Authors
  1. Xiao Ma
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  2. Hongwei Lu
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  3. Zaobin Gan
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Corresponding author

Correspondence to Zaobin Gan .

Editor information

Editors and Affiliations

  1. Tsinghua University, Bijing, China

    Jianyong Wang

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. Florida Atlantic University, Boca Raton, Florida, USA

    Dingding Wang

  4. Victoria University, Melbourne, Australia

    Hua Wang

  5. School of Computing & Information, Florida International University, Miami, Florida, USA

    Shu-Ching Chen

  6. Florida International University, Miami, Florida, USA

    Tao Li

  7. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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© 2015 Springer International Publishing Switzerland

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Ma, X., Lu, H., Gan, Z. (2015). Implicit Trust and Distrust Prediction for Recommender Systems. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-26190-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26189-8

  • Online ISBN: 978-3-319-26190-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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