Abstract
In previous work we have proposed a data mining model to capture user web navigation patterns, which models the navigation sessions as a hypertext probabilistic grammar. The grammar’s higher probability strings correspond to the user preferred trails and an algorithm was given to find all strings with probability above a threshold. Herein, we propose a heuristic aimed at finding longer trails composed of links whose average probability is above the threshold. A dynamic threshold is provided whose value is at all times proportional to the length of the trail being evaluated. We report on experiments with both real and synthetic data which were conducted to assess the heuristic’s utility.
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Borges, J., Levene, M. (2000). A Heuristic to Capture Longer User Web Navigation Patterns. In: Bauknecht, K., Madria, S.K., Pernul, G. (eds) Electronic Commerce and Web Technologies. EC-Web 2000. Lecture Notes in Computer Science, vol 1875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44463-7_14
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DOI: https://doi.org/10.1007/3-540-44463-7_14
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