Predicting Web User’s Behavior: An Absorbing Markov Chain Approach

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 296)


We develop a novel predictive modeling framework for Web user behavior with web usage mining (WUM). The proposed predictive model utilizes sequence-based clustering, to group Web users into clusters with similar Web browsing behavior, and absorbing Markov chains (AMC) in order to model Web users’ navigation behavior. Clustering facilitates the prediction of Web users’ navigation behavior by identifying groups of Web users showing similar browsing patterns. The use of AMC allows calculation of transition probabilities and absorbing probabilities at any given time of active user sessions, and thus leads to a better Web personalization and a more effective online advertising outcome. This research will also provide a performance evaluation framework along with the proposed model and suggest a WUM system that can improve ad placement and target marketing in a website.


Web mining Predictive analytics Markov chain Clustering 



This research is funded in part by a Belk College Summer Research grant from the Belk College of Business, UNC Charlotte.


  1. 1.
    Ho, S.Y., Bodoff, D., Tam, K.Y.: Timing of adaptive web personalization and its effects on online consumer behavior. Inf. Syst. Res. 22(3), 660–679 (2011)CrossRefGoogle Scholar
  2. 2.
    Facca, F.M., Lanzi, P.L.: Mining interesting knowledge from weblogs: a survey. Data Knowl. Eng. 53(3), 225–241 (2005)CrossRefGoogle Scholar
  3. 3.
    Pierrakos, D., Paliouras, G., Papatheodorou, C., Spyropoulos, C.D.: Web usage mining as a tool for personalization: a survey. User Model. User-Adap. Inter. 13(4), 311–372 (2003)CrossRefGoogle Scholar
  4. 4.
    Kim, Y.: Weighted order-dependent clustering and visualization of web navigation patterns. Decis. Support Syst. 43(4), 1630–1645 (2007)CrossRefGoogle Scholar
  5. 5.
    Kumar, P., Krishna, P.R., Bapi, R.S., De, S.K.: Rough clustering of sequential data. Data Knowl. Eng. 63(2), 183–199 (2007)CrossRefGoogle Scholar
  6. 6.
    Park, S., Suresh, N.C., Jeong, B.-K.: Sequence-based clustering for Web usage mining: a new experimental framework and ANN-enhanced K-means algorithm. Data Knowl. Eng. 65(3), 512–543 (2008)CrossRefGoogle Scholar
  7. 7.
    Shahabi, C., Banaei-Kashani, F.: Efficient and anonymous web-usage mining for web personalization. INFORMS J. Comput. 15(2), 123–147 (2003)CrossRefMATHGoogle Scholar
  8. 8.
    Hung, Y.-S., Chen, K.-L.B., Yang, C.-T., Deng, G.-F.: Web usage mining for analysing elder self-care behavior patterns. Expert Syst. Appl. 40(2), 775–783 (2013)CrossRefGoogle Scholar
  9. 9.
    Borges, J., Levene, M.: Generating dynamic higher-order Markov models in web usage mining. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS, vol. 3721, pp. 34–45. Springer, Heidelberg (2005). doi: 10.1007/11564126_9 CrossRefGoogle Scholar
  10. 10.
    Da Silva, A., Lechevallier, Y., de Carvalho, F., Trousse, B.: Mining web usage data for discovering navigation clusters. In: Proceedings of 11th IEEE Symposium on Computers and Communications, ISCC 2006, pp. 910–915. IEEE (2006)Google Scholar
  11. 11.
    Cadez, I., Heckerman, D., Meek, C., Smyth, P., White, S.: Model-based clustering and visualization of navigation patterns on a web site. Data Min. Knowl. Discov. 7, 399–424 (2003)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Deshpande, M., Karypis, G.: Selective Markov models for predicting web-page accesses. ACM Trans. Internet Technol. 4(2), 163–184 (2004)CrossRefGoogle Scholar
  13. 13.
    Sarukkai, R.R.: Link prediction and path analysis using Markov chains. Comput. Networks 33(1), 377–386 (2000)CrossRefGoogle Scholar
  14. 14.
    Grinstead, C.M., Snell, J.L.: Introduction to Probability. American Mathematical Soc., Providence (2012)MATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The Belk College of BusinessThe University of North Carolina at CharlotteCharlotteUSA

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