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Trustable aggregation of online ratings

Published: 27 October 2013 Publication History

Abstract

The average of the customer ratings on the product, which we call reputation, is one of the key factors in online purchasing decision of a product. There is, however, no guarantee in the trustworthiness of the reputation since it can be manipulated rather easily. In this paper, we define false reputation as the problem of the reputation to be manipulated by unfair ratings, and design a general framework that provides trustable reputation. For this purpose, we propose TRUEREPUTATION, an algorithm that iteratively adjusts the reputation based on the confidence of customer ratings.

References

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S. Liu, J. Zhang, C. Miao, Y. Theng, and A. Kot, "iCLUB: an Integrated Clustering-based Approach to Improve the Robustness of Reputation Systems," AAMAS, pp. 1151--1152, 2011.
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B. Mobasher, R. Burke, R. Bhaumik, and C. Williams, "Towards Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness," ACM Trans. on Internet Technology, Vol. 7, No. 2, pp. 1--40, 2007.
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Cited By

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  • (2019)A unified framework of trust prediction based on message passingCluster Computing10.1007/s10586-018-1807-x22:1(2049-2061)Online publication date: 1-Jan-2019
  • (2016)Robust Features for Trustable Aggregation of Online RatingsProceedings of the 10th International Conference on Ubiquitous Information Management and Communication10.1145/2857546.2857560(1-7)Online publication date: 4-Jan-2016

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  1. Trustable aggregation of online ratings

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    cover image ACM Conferences
    CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
    October 2013
    2612 pages
    ISBN:9781450322638
    DOI:10.1145/2505515
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 27 October 2013

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    Author Tags

    1. false reputation
    2. robustness
    3. trust
    4. unfair ratings

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    CIKM'13
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    CIKM'13: 22nd ACM International Conference on Information and Knowledge Management
    October 27 - November 1, 2013
    California, San Francisco, USA

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    CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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    Cited By

    View all
    • (2019)A unified framework of trust prediction based on message passingCluster Computing10.1007/s10586-018-1807-x22:1(2049-2061)Online publication date: 1-Jan-2019
    • (2016)Robust Features for Trustable Aggregation of Online RatingsProceedings of the 10th International Conference on Ubiquitous Information Management and Communication10.1145/2857546.2857560(1-7)Online publication date: 4-Jan-2016

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