A Survey of Model Used for Web User’s Browsing Behavior Prediction

Abstract

The motivation behind the work is that the prediction of web user’s browsing behavior while serving the Internet, reduces the user’s browsing access time and avoids the visit of unnecessary pages to ease network traffic. Various models such as fuzzy interference models, support vector machines (SVMs), artificial neural networks (ANNs), association rule mining (ARM), k-nearest neighbor(kNN) Markov model, Kth order Markov model, all-Kth Markov model and modified Markov model were proposed to handle Web page prediction problem. Many times, the combination of two or more models were used to achieve higher prediction accuracy. This research work introduces the Support Vector Machines for web page prediction. The advantages of using support vector machines is that it offers most robust and accurate classification due to their generalized properties with its solid theoretical foundation and proven effectiveness. Web contains enormous amount of data and web data increases exponentially but the training time for Support vector machine is very large. That is, SVM’s suffer from a widely recognized scalability problem in both memory requirement and computation time when the input dataset is too large. To address this, I aimed at training the Support vector machine model in MapReduce programming model of Hadoop framework, since the MapReduce programming model has the ability to rapidly process large amount of data in parallel. MapReduce works in tandem with Hadoop Distributed File System (HDFS). So proposed approach will solve the scalability problem of present SVM algorithm.

Keywords:Web Page Prediction, Support Vector Machines, Hadoop, MapReduce, HDFS.