A robust and fast reputation system for online rating systems

Mohsen Rezvani*, Mojtaba Rezvani

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    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.

    Original languageEnglish
    Title of host publicationWeb Information Systems Engineering – WISE 2017 - 18th International Conference, Proceedings
    EditorsWeijia Jia, Andrey Klimenko, Fedor Dzerzhinskiy, Stanislav V. Klimenko, Lu Chen, Qing Li, Yunjun Gao, Athman Bouguettaya, Xiangliang Zhang
    PublisherSpringer Verlag
    Pages175-183
    Number of pages9
    ISBN (Print)9783319687858
    DOIs
    Publication statusPublished - 2017
    Event18th International Conference on Web Information Systems Engineering, WISE 2017 - Puschino, Russian Federation
    Duration: 7 Oct 201711 Oct 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10570 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference18th International Conference on Web Information Systems Engineering, WISE 2017
    Country/TerritoryRussian Federation
    CityPuschino
    Period7/10/1711/10/17

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