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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References
Ageev, M., Guo, Q., Lagun, D., Agichtein, E.: Find it if you can: a game for modeling different types of web search success using interaction data. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2011), pp. 345–354. ACM, New York (2011)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Heath, A.P., White, R.W.: Defection detection: predicting search engine switching. In: Proceedings of the 17th International Conference on World Wide Web, pp. 1173–1174. ACM, New York (2008)
Kalinin, P.: Neural networks applied to switching prediction. Voronezh State University. In: The Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM 2013), Rome, Italy, February 2013
Kendall, M., Gibbons, J.D.: Rank Correlation Methods, 5th edn. Oxford University Press, Oxford (1990)
Levenshtein, V.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10, 707 (1966)
Ling, C., Huang, J., Zhang, H.: AUC: a statistically consistent and more discriminating measure than accuracy. In: International Joint Conference on Artificial Intelligence, vol. 18, pp. 519–526 (2003)
Yan, Q., Wang, X., Qiang, X., Kong, D., Bickson, D., Yuan, Q., Yang, Q.: Predicting search engine switching in WSCD 2013 challenge. In: Workshop on Web Search Click Data (WSCD), Rome, Italy, February 2013
Rajaraman, A.: Jeffrey David Ullman: Mining of Massive Datasets. Cambridge University Press, New York (2011)
Raskin, A.: Comparison of partial orders clustering techniques. Proc. ISP RAS 26(4), 91–98 (2014)
Savenkov, D., Dmitry, L., Liu, Q.: Search engine switching detection based on user personal preferences and behavior patterns. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, 28 July–01 August 2013 (2013)
Scherbina, A., Kuznetsov, S.: Clustering of web sessions using levenshtein metric. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275, pp. 127–133. Springer, Heidelberg (2004)
Wagner, R., Fischer, M.: The string-to-string correction problem. J. ACM. 21(1), 168–173 (1974)
White, R.W., Dumais, S.T.: Characterizing and predicting search engine switching behavior. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM 2009), pp. 87–96. ACM, New York (2009)
Ukkonen, A.: Clustering algorithms for chains. J. Mach. Learn. Res. 12, 1389–1423 (2011)
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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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