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Efficient web navigation prediction using hybrid models based on multiple evidence combinations

Modeling user(s) navigation sequences and predicting their preferences has been an interesting area of research. For Web Navigation Prediction (WNP) the Markov model(s) are predominantly used for analyzing and discovering user navigation patterns. One of the major issues with the Markov model is that it fails to predict for unclassified navigations. Presence of such navigations reduces the prediction power of the model. Deep machine learning models can be used to address unclassified navigations but their prediction ability deteriorates if training sessions are less in number. As Navigations have been modeled using N-Grams where the number of training sessions reduces at higher N-Grams. It might affect the performance of deep learning models. However, their prediction ability can be improvised by integrating it with the Markov model. This paper proposes three integrated models to minimize the unclassified navigations and to boost the overall prediction accuracy. Proposed hybrid models are formed by integrating All-Kth Markov Model with Deep Neural Network (DKM) and All-Kth Modified Markov Model with Shallow Neural Network and Deep Neural Network (SKMM and DKMM). The proposed models are evaluated on three standard datasets: CTI, BMS, and Wikispeedia. DKMM has obtained the best results in terms of improvement in prediction accuracy and reduction in unclassified navigations on higher N-grams. Prediction accuracy was improved up to 4.71, 6.2 and 7.67 in CTI, BMS and Wikispeedia dataset.