IDEAS home Printed from https://ideas.repec.org/a/bla/jamist/v58y2007i9p1254-1266.html
   My bibliography  Save this article

User rankings of search engine results

Author

Listed:
  • Judit Bar‐Ilan
  • Kevin Keenoy
  • Eti Yaari
  • Mark Levene

Abstract

In this study, we investigate the similarities and differences between rankings of search results by users and search engines. Sixty‐seven students took part in a 3‐week‐long experiment, during which they were asked to identify and rank the top 10 documents from the set of URLs that were retrieved by three major search engines (Google, MSN Search, and Yahoo!) for 12 selected queries. The URLs and accompanying snippets were displayed in random order, without disclosing which search engine(s) retrieved any specific URL for the query. We computed the similarity of the rankings of the users and search engines using four nonparametric correlation measures in [0,1] that complement each other. The findings show that the similarities between the users' choices and the rankings of the search engines are low. We examined the effects of the presentation order of the results, and of the thinking styles of the participants. Presentation order influences the rankings, but overall the results indicate that there is no “average user,” and even if the users have the same basic knowledge of a topic, they evaluate information in their own context, which is influenced by cognitive, affective, and physical factors. This is the first large‐scale experiment in which users were asked to rank the results of identical queries. The analysis of the experimental results demonstrates the potential for personalized search.

Suggested Citation

  • Judit Bar‐Ilan & Kevin Keenoy & Eti Yaari & Mark Levene, 2007. "User rankings of search engine results," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(9), pages 1254-1266, July.
  • Handle: RePEc:bla:jamist:v:58:y:2007:i:9:p:1254-1266
    DOI: 10.1002/asi.20608
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.20608
    Download Restriction: no

    File URL: https://libkey.io/10.1002/asi.20608?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. Maayan Zhitomirsky-Geffet & Judit Bar-Ilan & Mark Levene, 2016. "A Markov Chain Model for Changes in Users’ Assessment of Search Results," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-14, May.

    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:jamist:v:58:y:2007:i:9:p:1254-1266. 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.