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    Rank-Biased Precision for Measurement of Retrieval Effectiveness

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    Author
    Moffat, A; Zobel, J
    Date
    2009-01-01
    Source Title
    ACM Transactions on Information Systems
    Publisher
    ASSOC COMPUTING MACHINERY
    University of Melbourne Author/s
    Moffat, Alistair; Zobel, Justin
    Affiliation
    Computer Science and Software Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Moffat, A. & Zobel, J. (2009). Rank-Biased Precision for Measurement of Retrieval Effectiveness. ACM TRANSACTIONS ON INFORMATION SYSTEMS, 27 (1), https://doi.org/10.1145/1416950.1416952.
    Access Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/29907
    DOI
    10.1145/1416950.1416952
    Abstract
    <jats:p> A range of methods for measuring the effectiveness of information retrieval systems has been proposed. These are typically intended to provide a quantitative single-value summary of a document ranking relative to a query. However, many of these measures have failings. For example, recall is not well founded as a measure of satisfaction, since the user of an actual system cannot judge recall. Average precision is derived from recall, and suffers from the same problem. In addition, average precision lacks key stability properties that are needed for robust experiments. In this article, we introduce a new effectiveness metric, <jats:italic>rank-biased precision</jats:italic> , that avoids these problems. Rank-biased pre-cision is derived from a simple model of user behavior, is robust if answer rankings are extended to greater depths, and allows accurate quantification of experimental uncertainty, even when only partial relevance judgments are available. </jats:p>
    Keywords
    Information Systems

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