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Temporal analysis of a very large topically categorized Web query log.

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Title: Temporal analysis of a very large topically categorized Web query log.
Authors: Beitzel, Steven M.1 steve@ir.iit.edu, Jensen, Eric C.1 ej@ir.iit.edu, Chowdhury, Abdur1 abdur@ir.iit.edu, Frieder, Ophir1 ophir@ir.iit.edu, Grossman, David1 dagr@ir.iit.edu
Source: Journal of the American Society for Information Science & Technology. Jan2007, Vol. 58 Issue 2, p166-178. 13p. 4 Charts, 24 Graphs.
Subjects: Internet questionnaires, World Wide Web, Internet searching, Information storage & retrieval systems, Information retrieval, Database searching, Editors, Information science
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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Data: Temporal analysis of a very large topically categorized Web query log.
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  Data: <searchLink fieldCode="AR" term="%22Beitzel%2C+Steven+M%2E%22">Beitzel, Steven M.</searchLink><relatesTo>1</relatesTo><i> steve@ir.iit.edu</i><br /><searchLink fieldCode="AR" term="%22Jensen%2C+Eric+C%2E%22">Jensen, Eric C.</searchLink><relatesTo>1</relatesTo><i> ej@ir.iit.edu</i><br /><searchLink fieldCode="AR" term="%22Chowdhury%2C+Abdur%22">Chowdhury, Abdur</searchLink><relatesTo>1</relatesTo><i> abdur@ir.iit.edu</i><br /><searchLink fieldCode="AR" term="%22Frieder%2C+Ophir%22">Frieder, Ophir</searchLink><relatesTo>1</relatesTo><i> ophir@ir.iit.edu</i><br /><searchLink fieldCode="AR" term="%22Grossman%2C+David%22">Grossman, David</searchLink><relatesTo>1</relatesTo><i> dagr@ir.iit.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Society+for+Information+Science+%26+Technology%22">Journal of the American Society for Information Science & Technology</searchLink>. Jan2007, Vol. 58 Issue 2, p166-178. 13p. 4 Charts, 24 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Internet+questionnaires%22">Internet questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22World+Wide+Web%22">World Wide Web</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+searching%22">Internet searching</searchLink><br /><searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Database+searching%22">Database searching</searchLink><br /><searchLink fieldCode="DE" term="%22Editors%22">Editors</searchLink><br /><searchLink fieldCode="DE" term="%22Information+science%22">Information science</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/asi.20464
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        Text: English
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        PageCount: 13
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      – SubjectFull: Internet questionnaires
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      – SubjectFull: World Wide Web
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      – SubjectFull: Internet searching
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      – TitleFull: Temporal analysis of a very large topically categorized Web query log.
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              Text: Jan2007
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