Predicting the browsing behavior of the user on the web has gained significant importance, as it improves the productivity of the website owners and also raises the interest of web users. The Markov model has been used immensely for user’s web navigation prediction. To enhance the coverage and accuracy of the Markov model, higher order Markov models are integrated with lower order models. However, this integration results in large state-space complexity. To reduce the state-space complexity, this paper proposes a novel technique, namely Pruned all-Kth modified Markov model (PKM3). PKM3 eliminates the irrelevant states from a higher order model, which have a negligible contribution toward prediction. The proposed model is evaluated on four standard weblogs: BMS, MSWEB, CTI and MSNBC. PKM3 performance was optimal for the website in which pages were closely placed and share high interlinking. This pruning-based optimal model achieves a significant reduction in state-space complexity while maintaining comparable accuracy.
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Jindal, H., Sardana, N. PKM3: an optimal Markov model for predicting future navigation sequences of the web surfers. Pattern Anal Applic (2020). https://doi.org/10.1007/s10044-020-00892-7
- All-Kth modified
- Markov model