A New Reactive Framework for Web Using Data Analaysis

Creating a Web Personalization system is the process of tailoring a website to meet the specific requirements of an individual user or a group of users. To create accurate predictions about users' future movements, association rule mining and sequential pattern finding are some of the approaches it employs, clustering, and classification, among others. Recent approaches for online customization have fallen short in terms of accurately predicting user interests. An innovative online customization approach has been created in order to give useful suggestions. A website's web pages must be given suitable weights, and this is the foundation of the suggested work. In order to do this, we employed the distance measure of the visit relationship as well as the occurrence frequency measure of web pages. The technique makes use of an improved graph-based partitioning algorithm for grouping web pages and classifying the current user actions more precisely than previous approaches. When making a judgement between web sites for the purpose of recommendation, a Threshold value is employed. Our testing findings show that accuracy is around 61 percent, coverage is approximately 34 percent, and F1 measure is 44.23 percent. It contributes to the enhancement of the user's browsing experience.