Abstract
Deciding about the best action plan to be tailored to and carried out for each user is the key for personalization. This is a challenging task as the maximum number of elements of an environment have to be taken into account when making decisions such as type of user, actual behaviour or goals to fulfil. The difficulty is even greater when dealing with web users and when decisions have to be taken on-line and salesmen are not involved. In this paper, we propose an approach that integrates user typologies and behaviour patterns in a multidepartamental organization to decide the best action plan to be carried out at each particular moment. The key idea to do this is based on detecting users behaviour changes by means of Behaviour Evolution Models (a combination of Discrete Markov Models). Besides, an agent based architecture has been proposed for the implementation of the whole method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mersereau, A.J., Bertsimas, D.J., Patel, N.R.: Dynamic classification of online customers. In: Proceedings of the SIAM International Conference on Data Mining, San Francisco, California, May
Weld, D., Anderson, C., Domingos, P.: Relational markov models and their applications to adaptive web navigation. In: Proc. of The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002 (2002)
Dai, H., Mobasher, B.: A road map to more effective web personalization: Integrating domain knowledge with web usage mining. In: Proc. of the International Conference on Internet Computing 2003 (IC 2003), Las Vegas, Nevada (June 2003)
Deshpande, M., Karypis, G.: Selective markov models for predicting web-page accesses (2001)
Etzioni, O.: The world-wide web: Quagmire or gold mine? Communications of the ACM 39(11), 65–68 (1996)
Pérez, M., Hochsztain, E., Robles, V., Marbán, O., Peña, J., Tasistro, A., Menasalvas, E., Millán, S.: Beyond user clicks: an algorithm and an agent-based architecture to discover user behavior. In: 1st European Web Mining Forum, Workshop at ECML/PKDD 2003, Cavtat-Dubrovnik, Croatia, September 22 (2003)
Fu, Y., Sandhu, K., Shih, M.: Clustering of web users based on access patterns (1999)
Hadjimichael, M., Marbán, O., Menasalvas, E., Millan, S., Peña, J.M.: Subsessions: a granular approach to click path analysis. In: Proceedings of IEEE Int. Conf. On Fuzzy Systems 2002 (WCCI 2002), Honolulu, U.S.A., May 2002, pp. 878–883 (2002)
Huberman, B.A., Pirolli, P.L.T., Pitkow, J.E., Lukose, R.M.: Strong regularities in World Wide Web surfing. Science 280(5360), 95–97 (1998)
Meek, C., Smyth, P., White, S., Cadez, I., Heckerman, D.: Visualization of navigations patterns on a web site using model-based clustering. In: Proc. of The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2000 (2000)
Kraiss, A., Weikum, G.: Integrated document caching and prefetching in storage hierarchies based on Markov-chain predictions. VLDB Journal: Very Large Data Bases 7(3), 141–162 (1998)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M., Witshire, J.: Discovery of aggregate usage profiles for web personalization. In: Proceedings of the WebKDD Workshop (2000)
Nasraoiu, O., Krisnapuram, R., Joshi, A.: Mining web access logs using a fuzzy relational clustering algorithm based on a robust estimator (1998)
Nasraoui, O., Frigui, H., Joshi, A., Krishnapuram, R.: Mining web access logs using relational competitive fuzzy clustering
Perkowitz, M., Etzioni, O.: Adaptive web sites: Automatically synthesizing web pages. In: AAAI/IAAI, pp. 727–732 (1998)
Perkowitz, M., Etzioni, O.: Towards adaptive Web sites: conceptual framework and case study. Computer Networks 31(11-16), 1245–1258 (1999)
Pitkow, J.E., Pirolli, P.: Mining longest repeating subsequences to predict world wide web surfing. In: USENIX Symposium on Internet Technologies and Systems (1999)
Rabiner, L.R.:
Sarukkai, R.R.: Link prediction and path analysis using markov chains. Computer Networks 33(1-6), 377–386
Sarukkai, R.: Link prediction and path analysis using markov chains. In: Ninth International World Wide Web Conference (2000)
Shahabi, C., Zarkesh, A.M., Adibi, J., Shah, V.: Knowledge discovery from user’s webpage navigation. In: Proceedings of the Seventh InternationalWorkshop on Research Issues in Data Engineering, High Performance Database Management for Large-Scale Applications (RIDE 1997), Washington- Brussels - Tokyo, pp. 20–31. IEEE, Los Alamitos (1997)
Mogul, J.C., Padmanabhan, V.N.: Using predictive prefetching to improve world wide web latency. Computer Communication Review (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Menasalvas, E., Millán, S., Gonzalez, P. (2004). Using Markov Models to Define Proactive Action Plans for Users at Multi-viewpoint Websites. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_95
Download citation
DOI: https://doi.org/10.1007/978-3-540-25929-9_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
Online ISBN: 978-3-540-25929-9
eBook Packages: Springer Book Archive