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Defining a session on Web search engines

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  • Bernard J. Jansen
  • Amanda Spink
  • Chris Blakely
  • Sherry Koshman

Abstract

Detecting query reformulations within a session by a Web searcher is an important area of research for designing more helpful searching systems and targeting content to particular users. Methods explored by other researchers include both qualitative (i.e., the use of human judges to manually analyze query patterns on usually small samples) and nondeterministic algorithms, typically using large amounts of training data to predict query modification during sessions. In this article, we explore three alternative methods for detection of session boundaries. All three methods are computationally straightforward and therefore easily implemented for detection of session changes. We examine 2,465,145 interactions from 534,507 users of Dogpile.com on May 6, 2005. We compare session analysis using (a) Internet Protocol address and cookie; (b) Internet Protocol address, cookie, and a temporal limit on intrasession interactions; and (c) Internet Protocol address, cookie, and query reformulation patterns. Overall, our analysis shows that defining sessions by query reformulation along with Internet Protocol address and cookie provides the best measure, resulting in an 82% increase in the count of sessions. Regardless of the method used, the mean session length was fewer than three queries, and the mean session duration was less than 30 min. Searchers most often modified their query by changing query terms (nearly 23% of all query modifications) rather than adding or deleting terms. Implications are that for measuring searching traffic, unique sessions may be a better indicator than the common metric of unique visitors. This research also sheds light on the more complex aspects of Web searching involving query modifications and may lead to advances in searching tools.

Suggested Citation

  • Bernard J. Jansen & Amanda Spink & Chris Blakely & Sherry Koshman, 2007. "Defining a session on Web search engines," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(6), pages 862-871, April.
  • Handle: RePEc:bla:jamist:v:58:y:2007:i:6:p:862-871
    DOI: 10.1002/asi.20564
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    Cited by:

    1. Roy, Gobinda & Sharma, Swati, 2021. "Measuring the role of factors on website effectiveness using vector autoregressive model," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).

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