A Recommender System for the Web: Using User Profiles and Machine Learning Methods

Web development without an integrated structure makes lots of difficulties for users. Web personalization systems are presented to make the website compatible with interest of users in both aspects of contents and services. In this paper extracting user navigation patterns is used to capture similar behaviors of users in order to increase the quality of recommendations. Based on patterns extracted from the same user navigation, recommendations are provided to the user to make it easier to navigate. Recently, web browsing techniques have been widely used for personalization. In this study, a method is proposed to create a user profile with the web usage mining by clustering and neural networks in order to predict the user's future requests and then generate a list of the pages of user's favorites. Simulation results shows that proposed method will increase the accuracy of recommender systems.