Recommendation of Web Pages using Weighted K-Means Clustering

Web Recommendation Systems are implemented by using collaborative filtering approach. It is a specific type of information filtering system that aims to predict the user browsing activity and then recommend to the user web pages items that are likely to be of interest. In this paper, a new recommendation system is proposed by using Weighted K-Means clustering approach to predict the user's navigational behavior. The proposed recommendation system based on Weighted K-Means clustering performs well when compared to K-Means algorithm. The performance of the comparative analysis is presented through experimental results.