A hybrid approach for personalized recommendation of news on the Web

A hybrid method for personalized recommendation of news on the Web is presented, which provides Web users with an autonomous tool that is able to minimize repetitive and tedious Web surfing. The proposed approach classifies Web pages by calculating the respective weights of terms. A user’s interest and preference models are generated by analyzing the user’s navigational history. Based on the content of the Web pages and on a user’s interest and preference models, the recommender system suggests news Web pages to the user who is likely interested in the related topics. Moreover, the technique of collaborative filtering, which aims to choose the trusted users, is employed to improve the performance of the recommender system. Experiments are carried out in order to demonstrate the effectiveness of the proposed method. In the experiments, Web news items are classified and recommended to Web users by matching the users’ interests with the contents of the news.