IDEAS home Printed from https://ideas.repec.org/a/bla/jamist/v58y2007i2p166-178.html
   My bibliography  Save this article

Temporal analysis of a very large topically categorized Web query log

Author

Listed:
  • Steven M. Beitzel
  • Eric C. Jensen
  • Abdur Chowdhury
  • Ophir Frieder
  • David Grossman

Abstract

The authors review a log of billions of Web queries that constituted the total query traffic for a 6‐month period of a general‐purpose commercial Web search service. Previously, query logs were studied from a single, cumulative view. In contrast, this study builds on the authors' previous work, which showed changes in popularity and uniqueness of topically categorized queries across the hours in a day. To further their analysis, they examine query traffic on a daily, weekly, and monthly basis by matching it against lists of queries that have been topically precategorized by human editors. These lists represent 13% of the query traffic. They show that query traffic from particular topical categories differs both from the query stream as a whole and from other categories. Additionally, they show that certain categories of queries trend differently over varying periods. The authors key contribution is twofold: They outline a method for studying both the static and topical properties of a very large query log over varying periods, and they identify and examine topical trends that may provide valuable insight for improving both retrieval effectiveness and efficiency.

Suggested Citation

  • Steven M. Beitzel & Eric C. Jensen & Abdur Chowdhury & Ophir Frieder & David Grossman, 2007. "Temporal analysis of a very large topically categorized Web query log," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(2), pages 166-178, January.
    • Handle: RePEc:bla:jamist:v:58:y:2007:i:2:p:166-178
    DOI: 10.1002/asi.20464
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.20464
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asi.20464?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. Russell Miller & Nicholas Doria-Anderson & Akira Shibanuma & Jennifer Lisa Sakamoto & Aya Yumino & Masamine Jimba, 2021. "Evaluating Local Multilingual Health Care Information Environments on the Internet: A Pilot Study," IJERPH, MDPI, vol. 18(13), pages 1-12, June.

    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:2:p:166-178. 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.