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A fine grained heuristic to capture web navigation patterns

A fine grained heuristic to capture web navigation patterns A Fine Grained Heuristic to Capture Web Navigation Patterns Jos6 Borges and Mark Levene Department of ComputerScience, UniversityCollege London, Gower Street, London WC1E 6BT, U.K. {j.borges, mlevene} ~cs.ucl.ac.uk ABSTRACT In previous work we have proposed a statistical model to capture the user behaviour when browsing the web. The user navigation information, obtained from web logs, is modelled as a hypertext probabilistic grammar (HPG) which is within the class of regular probabilistic grammars. The set of highest probability strings generated by the grammar corresponds to the user preferred navigation trails. We have previously conducted experiments with a Breadth-First Search algorithm (BFS) to perform the exhaustive computation of all the strings with probability above a specified cut-point, which we call the rules. Although the algorithm's running time varies linearly with the number of grammar states, it has the drawbacks of returning a large number of rules when the cut-point is small and a small set of very short rules when the cut-point is high. In this work, we present a new heuristic that implements an iterative deepening search wherein the set of rules is incrementally augmented by first exploring trails with high probability. A stopping parameter is provided which measures http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGKDD Explorations Newsletter Association for Computing Machinery

A fine grained heuristic to capture web navigation patterns

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2000 by ACM Inc.
ISSN
1931-0145
DOI
10.1145/360402.360416
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Abstract

A Fine Grained Heuristic to Capture Web Navigation Patterns Jos6 Borges and Mark Levene Department of ComputerScience, UniversityCollege London, Gower Street, London WC1E 6BT, U.K. {j.borges, mlevene} ~cs.ucl.ac.uk ABSTRACT In previous work we have proposed a statistical model to capture the user behaviour when browsing the web. The user navigation information, obtained from web logs, is modelled as a hypertext probabilistic grammar (HPG) which is within the class of regular probabilistic grammars. The set of highest probability strings generated by the grammar corresponds to the user preferred navigation trails. We have previously conducted experiments with a Breadth-First Search algorithm (BFS) to perform the exhaustive computation of all the strings with probability above a specified cut-point, which we call the rules. Although the algorithm's running time varies linearly with the number of grammar states, it has the drawbacks of returning a large number of rules when the cut-point is small and a small set of very short rules when the cut-point is high. In this work, we present a new heuristic that implements an iterative deepening search wherein the set of rules is incrementally augmented by first exploring trails with high probability. A stopping parameter is provided which measures

Journal

ACM SIGKDD Explorations Newsletter – Association for Computing Machinery

Published: Jun 1, 2000

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