Utilization of Web Usage Features for Web Page Recommendation

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As the number of websites utilizations are increasing day by day so server load is also increasing directly. So inorder to increase the server utilization some of the web page which have less importance need to be identified and update. Considering these issue paper has developed an rank generation algorithm to find the important and useless pages on the servers. As famous markov modal technique was utilized for finding the access pattern of the web surfer. In this paper, Generalized Hyperbolic Functional is combined with markov modal he random surfer path. Experiment was done on large dataset where results shows that proposed combination gives higher accuracy as compare to the previous other approaches.

 The web is an important source of information retrieval now-a days, and the users accessing the web are from different backgrounds. The usage information about users are recorded in web logs. Analyzing web log files to extract useful patterns is called web usage mining. Web usage mining approaches include clustering, association rule mining, sequential pattern mining etc., To facilitate web page access by users, web recommendation model is needed. So the Interest in the analysis of user behavior on the Web has been increasing rapidly.

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