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A Robust and Fast Reputation System for Online Rating Systems

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Web Information Systems Engineering – WISE 2017 (WISE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10570))

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Abstract

Recent studies have shown that reputation escalation is emerging as a new service, by which dealers pay to receive good feedback and escalate their ratings in online shopping markets. With the dramatic increase in the number of ratings provided by consumers, scalability has arisen as a significant issue in the existing methods of reputation systems. In order to tackle such issue, we here propose a fast algorithm that calculates the reputation based on a random sample of the ratings. Since the randomly selected sample has a logarithmic size, it guarantees a feasible scalability for large-scale online review systems. In addition, the randomness nature of the algorithm makes it robust against unfair ratings. We analyze the effectiveness of the proposed algorithm through extensive empirical evaluation using real world and synthetically generated datasets. Our experimental results show that the proposed method provides a high accuracy while running much faster than the existing iterative filtering approach.

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Notes

  1. 1.

    http://grouplens.org/datasets/movielens/.

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Author information

Authors and Affiliations

  1. Shahrood University of Technology, Shahrood, Iran

    Mohsen Rezvani

  2. Australian National University, Canberra, Australia

    Mojtaba Rezvani

Authors
  1. Mohsen Rezvani
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  2. Mojtaba Rezvani
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Corresponding authors

Correspondence to Mohsen Rezvani or Mojtaba Rezvani .

Editor information

Editors and Affiliations

  1. University of Sydney, Darlington, NSW, Australia

    Athman Bouguettaya

  2. Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Institute of Computing for Physics and Technology, Protvino, Russia

    Andrey Klimenko

  4. Nanyang Technological University, Singapore, Singapore

    Lu Chen

  5. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Xiangliang Zhang

  6. Institute of Computing for Physics and Technology, Protvino, Russia

    Fedor Dzerzhinskiy

  7. Shanghai Jiao Tong University, Minhang Qu, China

    Weijia Jia

  8. Institute of Computing for Physics and Technology, Protvino, Russia

    Stanislav V. Klimenko

  9. City University of Hong Kong, Kowloon, Hong Kong

    Qing Li

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Rezvani, M., Rezvani, M. (2017). A Robust and Fast Reputation System for Online Rating Systems. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10570. Springer, Cham. https://doi.org/10.1007/978-3-319-68786-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-68786-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68785-8

  • Online ISBN: 978-3-319-68786-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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