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Using Levenshtein Distance for Typical User Actions and Search Engine Switching Detection

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Information Retrieval (RuSSIR 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 573))

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Abstract

This paper presents a new approach in automatic grouping of user search sessions. K-medoids clustering algorithm and Levenshtein distance function were used to group search sessions. We show that the groups obtained are meaningful and can be used to estimate the probability of user switching to another search engine. The proposed method was tested on real data provided by Yandex for 2012 Yandex Switching Detection Challenge and allowed for high AUC value (0.82 on internal tests). One more advantage of the presented approach is the possibility to visualize typical sequences of user action for simplified analyses of the data set.

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Author information

Authors and Affiliations

  1. National Research Nuclear University MEPhI, Kashirskoe sh. 31, 115409, Moscow, Russia

    Alexey Raskin & Petr Rudakov

Authors
  1. Alexey Raskin
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  2. Petr Rudakov
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Corresponding author

Correspondence to Alexey Raskin .

Editor information

Editors and Affiliations

  1. Ural Federal University , Yekaterinburg, Russia

    Pavel Braslavski

  2. University of Amsterdam, Amsterdam, The Netherlands

    Ilya Markov

  3. University of Florida , Gainsville, Florida, USA

    Panos Pardalos

  4. Eurecat , Barcelona, Spain

    Yana Volkovich

  5. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  6. National Research University Higher School of Economics, Saint Petersburg, Russia

    Sergei Koltsov

  7. National Research University Higher School of Economics, Saint Petersburg, Russia

    Olessia Koltsova

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Raskin, A., Rudakov, P. (2016). Using Levenshtein Distance for Typical User Actions and Search Engine Switching Detection. In: Braslavski, P., et al. Information Retrieval. RuSSIR 2015. Communications in Computer and Information Science, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-41718-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-41718-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41717-2

  • Online ISBN: 978-3-319-41718-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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