Abstract: Recommendation algorithms have been commonly adopted in many fields. However, traditional recommendation algorithms fail to achieve the expected recommendation results if they are applied to predict browsing behaviors of the mobile users and further to recommend personalized content to the mobile users. By analyzing the Internet browsing data of the mobile users, this paper proposes a recommendation model based on browsing data of mobile users, denoted as RMBDMU, to predict the future browsing activities of the mobile users and take them as the bases to recommend contents to the mobile users. An experiment on the Internet browsing behavior data of the real mobile users is conducted to verify the effectiveness of the model. The experiment result shows that the recommendation model based on browsing data of mobile users is more effective than the traditional recommendation algorithms.