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A Feature-Based Reputation Model for Product Evaluation

Author

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  • Umar Farooq

    (Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan‡DISP Laboratory, University Lumiere Lyon 2, 160 bd de l’Université, Bron cedex, Lyon, Rhne-Alpes, 69676, France§Department of Computer Science, Capital University of Science and Technology, Islamabad, 44000, Pakistan)

  • Antoine Nongaillard

    (#x2020;CRIStAL Laboratory, CNRS UMR 9189, Lille University, Cité Scientifique, Villeneuve d’Ascq, Lille, 59650, France)

  • Yacine Ouzrout

    (#x2021;DISP Laboratory, University Lumiere Lyon 2, 160 bd de l’Université, Bron cedex, Lyon, Rhne-Alpes, 69676, France)

  • Muhammad Abdul Qadir

    (#xA7;Department of Computer Science, Capital University of Science and Technology, Islamabad, 44000, Pakistan)

Abstract

Knowing the strengths and weaknesses of a product is very important for manufacturers and customers to make decisions. Several sentiment analysis systems are proposed to determine the opinions of customers about products and product features. However, the aggregation methods used are not able to estimate a true reputation value and to reflect the recent opinions quickly. Most of these systems are based on single source and therefore suffer from availability and susceptibility issues. In this paper, we propose a multi-source reputation model where several aggregation methods are introduced in order to evaluate product based on features. In addition, we also propose a method which uses four parameters in order to rank the reputability of each rating before considering it for reputation values. The results show that the proposed model estimates good reputation values even in the presence of biased behaviors, robust to false ratings and reflects the newest opinions about product rapidly.

Suggested Citation

  • Umar Farooq & Antoine Nongaillard & Yacine Ouzrout & Muhammad Abdul Qadir, 2016. "A Feature-Based Reputation Model for Product Evaluation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1521-1553, November.
    • Handle: RePEc:wsi:ijitdm:v:15:y:2016:i:06:n:s0219622016500358
    DOI: 10.1142/S0219622016500358
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    References listed on IDEAS

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    1. Gang Kou & Yanqun Lu & Yi Peng & Yong Shi, 2012. "Evaluation Of Classification Algorithms Using Mcdm And Rank Correlation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 197-225.
    2. H. Moulin, 1980. "On strategy-proofness and single peakedness," Public Choice, Springer, vol. 35(4), pages 437-455, January.
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