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In this paper, we propose a user profile model to describe users' preferences from multiple perspectives. Then, we discuss the degree of the user's preferences for historical news, and propose a method to calculate the preference weight of historical news according to the user's reading behavior and the popularity of news. This method could construct user profiles more accurately. Besides, we provide a dynamic method for news recommendation, in which both short-term and long-term user preferences are considered. The experimental results indicate that our method can significantly improve the recommendation effect.

A Dynamic Personalized News Recommendation System Based on BAP User Profiling Method