Personalization Web Pages for Site Users, Utilizing Users’ Interests and Sequential Patterns Discovery

zeynab fazelipour, Ali Harounabadi


With the rapid growth of information on the Web and increase of users who are daily visiting the web sites, presenting information proportionate to requirements of users who are visiting a special website so that they could find their desired information would be essential. Therefore, analyzing browsing behavior of web users and modeling this behavior has particular importance. The aim of recommender systems is guiding users to find their favorite resources and meet their needs, using the information obtained from the previous users’ interactions. In this paper, to predict the web pages with high precision, a hybrid algorithm of clustering technique, All-K th-Order Markov model, and neural network are presented. For this purpose, in order to model users’ movement behavior, after clustering those with the same interests, the sequential patterns are extracted on users’ sessions of each cluster using all-4th-order Markov model. Next, in the step of pages recommendation to a current user, which is performed in an online state, first, a current user session is assigned to a cluster using neural network. Then Markov model created on the cluster which has the nearest match to the current session, is applied and a sequence of pages, which the users are interested to view, is included in the list of recommendation. The implementation results demonstrate that the proposed algorithm has higher precision and recall comparing to other recommender systems.


Personalization Web Pages; Clustering; Neural Network; Markov Models

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