Wolf Optimization Based Web Page Prediction Using Weblog and Web Content Feature

Abstract

Website depends on user visits. To increase the user engagement on site web portals are working on page prediction algorithms. Many of researchers are working on this issue from last two decades. This paper has proposed a web page prediction model using web log feature. Raw weblog feature was pre-processed by applying Markova model with hierarchical structure. Markov model gives web page pattern with support value. User next web page prediction was done by wolf optimization genetic algorithm. Web mining known weblog and web content feature were used for obtaining the probability density value of web pages. This feature help algorithm to identify page having high opening probability. Wolf algorithm fitness function highly depends on this probability density function for selection of good chromosome in current population. Experiment was done on real weblog dataset. Results were compared with existing model on different evaluation parameters.