A novel web page recommender using data automatic clustering and Markov process

Abstract

As the web expands and data grow in the networks, finding useful knowledge from the World Wide Web becomes a major challenge. As intelligent systems, the recommender systems help users to find their favorite resource among a large body of information. This research aimed at providing a new page recommender system, which enhances the accuracy of the suggested pages based on the user interest, Automatic Clustering-based Genetic Algorithm (ACGA), and all modified 3th-order Markov Model. Therefore, the frequency and page visiting time by each user have been extracted from the log file of the web server, while the user interest in the page during a session has been computed by a linear combination. ACGA involved 2 phases of the cluster based on (1) similarity measure and (2) Genetic Algorithm. The novel cluster partitioned the vectorization sessions in separate clusters through a new fitness function. Moreover, all modified 3th-order Markov Model used all orders of Markov Model simultaneously, and predicted the next page in the page recommender system. The research did these experiments on a real CTI dataset. Experimental results and their comparison with other approaches showed superiority in the accuracy of the proposed system.