Effective Prediction ofWeb User Behaviour with User-Level Models

• Autorzy: Dembczyński K. , Kotłowski W.
• Języki publikacji: EN

Abstrakty

EN

The paper concerns the problem of predicting behaviour of web users, based on real historical data which constitutes an important issue in web mining. The research reported here was conducted while the authors participated in the international ECML/ PKDD 2007 Discovery Challenge competition – Track 1. The results presented here ended up as the winning solution to the contest. We describe the contest tasks and the real industrial datasets concerning the recorded behaviour of sample of Polish Web users on which our experiments were performed. We present the whole extensive experimental process from the data preprocessing phase to exploratory analysis of the data to the experimental comparison and discussion of various prediction models which we examined. As we explain, our solution has low time and space complexity, scales well with large datasets and, at the same time, produces high-quality results.

Słowa kluczowe

EN

user behaviour prediction; web mining; machine learning; experimentation; data analysis

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2008
• Tom: Vol. 89, nr 2-3
• Strony: 189--206
• Opis fizyczny: bibliogr. 26 poz., tab., wykr.

Twórcy

autor

Dembczyński K.

autor

Kotłowski W.

• Web Mining Lab, Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland,

Bibliografia

• [1] ECML/PKDD 2007 Discovery Challenge, Track 1 description (a copy), http://www.pjwstk.edu.pl/_msyd/predicting users behaviour.pdf.
• [2] Gemius S.A. Company, Warsaw, Poland, http://www.gemius.com.
• [3] GNU/Linux operating system and toolset, http://www.gnu.org.
• [4] Java programming language, http://java.sun.com/.
• [5] R statistical package, http://www.r-project.org.
• [6] Weka machine learning package, http://www.cs.waikato.ac.nz/ml/weka/.
• [7] Berger, J.: Statistical Decision Theory and Bayesian Analysis, Springer-Verlag, New York, 1993.
• [8] Dasu, T., Johnson, T.: Exploratory Data Mining and Data Cleaning, Wiley, 2003.
• [9] Dembczyński, K., Kotłowski, W., Sydow, M.: Effective Prediction ofWeb User Behaviour with User-Level Models, 2007.
• [10] Duda, R., Hart, P., Stork, D.: Pattern Classification, Second Edition, Wiley-Interscience, 2000.
• [11] ECML/PKDD'2007 Discovery Challenge: User's behaviour prediction, 2007, http://www.ecmlpkdd2007.org/challenge/.
• [12] Hamilton, J. D.: Time Series Analysis, Princeton University Press, 1994.
• [13] Hastie, T., Tibshirani, R., Friedman, J.: Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2003.
• [14] Hu, J., Zeng, H.-J., Li, H., Niu, C., Chen, Z.: Demographic prediction based on user's browsing behavior, WWW '07: Proceedings of the 16th international conference on World Wide Web, ACM, New York, NY, USA, 2007, ISBN 978- 1- 59593- 654-7.
• [15] Jaworska, J., Sydow, M.: Behavioural Targeting in On-line Advertising: An Empirical Study, 2008, Accepted for the 9th International Conference on Web Information Systems Engineering (Wise 2008), Auckland, New Zealand, September 1-4, 2008 (to be printed in LNCS, Springer).
• [16] Lee, T.-Y.: Predicting User's Behavior by the Frequent Items, Proceedings of the ECML/PKDD, Discovery Challenge, Warsaw 2007.
• [17] M.T. Hassan, K. J., Karim, A.: Bayesian Inference for Web Surfer Behavior Prediction, Proceedings of the ECML/PKDD, Discovery Challenge, Warsaw 2007.
• [18] Newcomb, K.: Search Marketing Shows Strength in 2006, searchenginewatch.com, 2007.
• [19] Ng, V., Mok, K.-H.: An Intelligent Agent for Web Advertisements, CODAS '01: Proceedings of the Third International Symposium on Cooperative Database Systems for Advanced Applications, IEEE Computer Society, Washington, DC, USA, 2001, ISBN 0-7695-1128-7.
• [20] Quinlan, J. R.: C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.
• [21] R Development Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, 2005.
• [22] Regelson, M., Fain, D. C.: Predicting click-through rate using keyword clusters, Proceedings of the Second Workshop on Sponsored Search Auctions, 2006.
• [23] Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads, WWW '07: Proceedings of the 16th International Conference on World Wide Web, ACM, New York, NY, USA, 2007.
• [24] Smith, S.: Behavioral targeting could change the game, January, 23 2007, Available at: http://www.econtentmag.com/Articles/ArticleReader.aspx?ArticleID=18964, accessed February 19, 2008.
• [25] Wald, A.: Statistical decision functions, Wiley, 1950.
• [26] Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, 2005.