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Markovian analysis for automatic new topic identification in search engine transaction logs

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  • Huseyin C. Ozmutlu

Abstract

Topic analysis of search engine user queries is an important task, since successful exploitation of the topic of queries can result in the design of new information retrieval algorithms for more efficient search engines. Identification of topic changes within a user search session is a key issue in analysis of search engine user queries. This study presents an application of Markov chains in the area of search engine research to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals, query reformulation patterns and the continuation/shift status of the previous query. The findings show that Markov chains provide fairly successful results for automatic new topic identification with a high level of estimation for topic continuations and shifts. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Huseyin C. Ozmutlu, 2009. "Markovian analysis for automatic new topic identification in search engine transaction logs," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(6), pages 737-768, November.
    • Handle: RePEc:wly:apsmbi:v:25:y:2009:i:6:p:737-768
    DOI: 10.1002/asmb.758
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    References listed on IDEAS

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    1. Amanda Spink & H. Cenk Ozmutlu & Seda Ozmutlu, 2002. "Multitasking information seeking and searching processes," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(8), pages 639-652.
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    4. Xiangji Huang & Fuchun Peng & Aijun An & Dale Schuurmans, 2004. "Dynamic Web log session identification with statistical language models," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(14), pages 1290-1303, December.
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    6. L. Di Scala & L. La Rocca & G. Consonni, 2004. "A Bayesian Hierarchical Model for the Evaluation of a Website," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(1), pages 15-27.
    7. Dietmar Wolfram & Amanda Spink & Bernard J. Jansen & Tefko Saracevic, 2001. "Vox populi: The public searching of the web," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(12), pages 1073-1074.
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