IDEAS home Printed from https://ideas.repec.org/a/bla/jamest/v46y1995i2p133-145.html
   My bibliography  Save this article

Measuring retrieval effectiveness based on user preference of documents

Author

Listed:
  • Y. Y. Yao

Abstract

The notion of user preference is adopted for the representation, interpretation, and measurement of the relevance or usefulness of documents. User judgments on documents may be formally described by a weak order (i.e., user ranking) and measured using an ordinal scale. Within this framework, a new measure of system performance is suggested based on the distance between user ranking and system ranking. It only uses the relative order of documents and therefore confirms to the valid use of an ordinal scale measuring relevance. It is also applicable to multilevel relevance judgments and ranked system output. The appropriateness of the proposed measure is demonstrated through an axiomatic approach. The inherent relationships between the new measure and many existing measures provide further supporting evidence. © 1995 John Wiley & Sons, Inc.

Suggested Citation

  • Y. Y. Yao, 1995. "Measuring retrieval effectiveness based on user preference of documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 46(2), pages 133-145, March.
    • Handle: RePEc:bla:jamest:v:46:y:1995:i:2:p:133-145
    DOI: 10.1002/(SICI)1097-4571(199503)46:23.0.CO;2-Z
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/(SICI)1097-4571(199503)46:23.0.CO;2-Z
    Download Restriction: no
    File URL: https://libkey.io/10.1002/(SICI)1097-4571(199503)46:23.0.CO;2-Z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 0. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 0, pages 1-42.
    2. Müge Akbulut & Yaşar Tonta & Howard D. White, 2020. "Related records retrieval and pennant retrieval: an exploratory case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 957-987, February.
    3. Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 2020. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 22(6), pages 1377-1418, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jamest:v:46:y:1995:i:2:p:133-145. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.asis.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.