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    DISI, University of Trento, Trento

    LiquidPub D3.2. Model of reviewers’ behavior in peer reviews

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    When users rate objects, a sophisticated algorithm that takes into account ability or reputation may produce a fairer or more accurate aggregation of ratings than the straightforward arithmetic average. Recently a number of authors have proposed different co-determination algorithms where both user and object reputation are iteratively refined together, permitting accurate measures of both to be derived directly from the rating data. These algorithms are of direct relevance to the LiquidPub project because they could find their application also in modern scientific publishing systems where scientists would be allowed to evaluate papers written by others. Using various artificial datasets, we perform a comparative test of several co-determination ranking algorithms and identify their respective realms of use. In most practical rating systems only a limited range of discrete values (such as the 5-star system of Amazon.com) is employed. We test different scales of discrete ratings and show that this seemingly minor modification in fact has a significant impact on algorithms’ performance. Paradoxically, where rating resolution is low, increased noise in users’ ratings may even improve the overall performance of the system.

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    Title : LiquidPub D3.2. Model of reviewers’ behavior in peer reviews
    Author(s) : Matus Medo, Joseph Wakeling
    Abstract : When users rate objects, a sophisticated algorithm that takes into account ability or reputation may produce a fairer or more accurate aggregation of ratings than the straightforward arithmetic average. Recently a number of authors have proposed different co-determination algorithms where both user and object reputation are iteratively refined together, permitting accurate measures of both to be derived directly from the rating data. These algorithms are of direct relevance to the LiquidPub project because they could find their application also in modern scientific publishing systems where scientists would be allowed to evaluate papers written by others. Using various artificial datasets, we perform a comparative test of several co-determination ranking algorithms and identify their respective realms of use. In most practical rating systems only a limited range of discrete values (such as the 5-star system of Amazon.com) is employed. We test different scales of discrete ratings and show that this seemingly minor modification in fact has a significant impact on algorithms’ performance. Paradoxically, where rating resolution is low, increased noise in users’ ratings may even improve the overall performance of the system.
    Keywords : LiquidPub, data mining, reputation, reputation systems, ranking

    Subject : unspecified
    Area : Computer Science
    Language : English
    Year : 2011

    Affiliations DISI, University of Trento, Trento
    Editors : Matus Medo
    Reviewers : Katsiaryna Mirylenka, Azzurra Ragone, Nardine Osman
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