Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions

by: J Borges, M Levene

Knowledge and Data Engineering, IEEE Transactions on, Vol. 19, No. 4. (2007), pp. 441-452.

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DOI, IEEE Explore, http://aps.arxiv.org/pdf/cs/0606115v1, http://aps.arxiv.org/abs/cs.AI/0606115

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Abstract

Markov models have been widely used to represent and analyze user Web navigation data. In previous work, we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable-length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable-length Markov model to summarize user Web navigation sessions up to a given length. Although the summarization ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalize a Web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarization ability

@article{citeulike:3017213, abstract = {Markov models have been widely used to represent and analyze user Web navigation data. In previous work, we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable-length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable-length Markov model to summarize user Web navigation sessions up to a given length. Although the summarization ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalize a Web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarization ability}, author = {Borges, J. and Levene, M. }, booktitle = {Knowledge and Data Engineering, IEEE Transactions on}, citeulike-article-id = {3017213}, doi = {10.1109/TKDE.2007.1012}, journal = {Knowledge and Data Engineering, IEEE Transactions on}, keywords = {clicks, time}, number = {4}, pages = {441--452}, posted-at = {2008-07-18 10:09:17}, priority = {1}, title = {Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions}, url = {http://dx.doi.org/10.1109/TKDE.2007.1012}, volume = {19}, year = {2007} }

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TY - JOUR ID - citeulike:3017213 L3 - citeulike-article-id:3017213 TI - Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions T2 - Knowledge and Data Engineering, IEEE Transactions on JF - Knowledge and Data Engineering, IEEE Transactions on VL - 19 IS - 4 SP - 441 EP - 452 N2 - Markov models have been widely used to represent and analyze user Web navigation data. In previous work, we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable-length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable-length Markov model to summarize user Web navigation sessions up to a given length. Although the summarization ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalize a Web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarization ability KW - clicks KW - time AU - Borges, J AU - Levene, M PY - 2007/// UR - http://dx.doi.org/10.1109/TKDE.2007.1012 ER -

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