WITHDRAWN: Experimental performance analysis of web recommendation model in web usage mining using KNN page ranking classification approach

Abstract

Abstract Web service is the technique of detecting suitable service as per requirements and it’s applicable in enterprises, industry and government. In the real time web applications, the web usage mining is mainly focused on determining the knowledge about users and market trends. The purpose of this research is to explore the efficient web service model which plays crucial role for acquiring different services. Initially, an input data is taken from the dataset which consist of different web pages. Then the pre-processing step is included for accessing the web contents by users. For this purpose, the process of tag based pre-processing is performed. Afterwards, the modification method is performed where the user can alter, change or modify the contents in the web pages. From this research, the classification method in web usage mining can be utilized to find visitor activity information in web server logs which helps the organization for getting very useful support information and to specify the evaluation of web applications. In this paper, we introduced a new version of the web page recommendation model in web mining based on KNN page ranking classification algorithm. The recommendation model proposed is validated with social media dataset and the obtained results are tested with necessary parameters. The experimental results show our proposed model offers better performance over the existing algorithms with the high accuracy rate.