Markov Model Based Web Page Recommendations by Combining Content and Log Features

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As the internet services are increasing day-byday various websites are working to find new techniques for webpage recommendations. Researchers have developed new methods for increasing the accuracy of predicted web pages. This paper has utilized web content feature of the website pages for developing the Term network where terms from each page help in specifying the relations between all pages. Web log is another feature used in this work where Markov model of third order helps in improving the prediction accuracy. Experiments are performed on different dataset sizes with these feature combinations. It is observed that proposed model is better as compared to previous works. Results show that the use of Markov Model with web content feature is better for prediction.

Published In : IJCAT Journal Volume 3, Issue 11

Date of Publication : November 2016

Pages : 510-514

Figures :02

Tables : 04

Neetu Sahu : Atal Bihari Vajpayee Hindi Vishwavidyalaya Bhopal, M.P., India

Pragyesh Kumar Agrawal : Institute for Excellence in Higher Education Bhopal, M.P., India

Information Extraction, Text Analysis, Feature Extraction, Text Categorization, Clustering

Internet has become the need of the modern world by providing lots of services and tools. So to increase its efficiency is primary requirement of the researchers. This paper has contributed the page prediction work by utilizing the weblog and web content features. Here web content is used for developing the relation between the terms in form of term network. In similar fashion weblog is used to find FWAP. It can be concluded from the tables presented in the result section that the proposed model provides better results as compared to previous models on different evaluation parameters. This work will be carried forward to increase the efficiency by using other features.

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