Exploiting Bat Algorithm to Optimize Association Rules for Web Personalization

Abstract

From a user search perspective, ranking of Web pages has always been a prime concern of a search engine because it helps to find the most pertinent and highly rated pages satisfying the search and need of users. There have been various measures and metrics which are used in deciding the ranks of web-pages in line with the quality of page and interest-score of the user. With the help of web usage analysis based on the user's past usage history, one can effectively improve the ranking of the web pages according to the user’s requirement. The objective of the proposed technique is to provide an efficient service mechanism for personalized web page ranking of search engines based on web usage analysis.

The web page personalization based on web mining technique has two major phases, offline and online. The objective of the offline phase is to model the user's navigation pattern using the history, web log data. The model is then used to predict a user navigation pattern and give recommendations to them based on prediction. Hybrid approach based on optimized association mining rule to get the ranking of webpages…To implement the proposed work in this paper the implicit information is collected from user on the basis of the past user activity data which is automatically stored in web server logs without their knowledge.

In this paper a hybrid approach based on optimized association mining rule is used to get the ranking of webpages. The proposed technique is implemented through exploiting BAT algorithm and an extensive comparative analysis and as well as validation is done along with the existing state of art techniques for web page personalization. The results thus achieved have shown a phenomenal performance in the target domain of research.