基于新闻时效性的协同过滤推荐算法

摘要

A collaborative filtering recommendation algorithm based on news timeliness is proposed. Firstly, by analyzing the characteristics of the news timeliness, the timeliness model of news is established. Then, the user-based collaborative filtering algorithm is improved combining the news timeliness model. Finally, the experimental results show that this method can highly enhance the performance of user-based collaborative filtering algorithm, and ameliorate the accuracy and recall rate of news recommendation.%提出一种基于新闻时效性的协同过滤推荐算法. 首先对新闻的时效性进行了特征分析, 建立了新闻时效性模型, 然后结合新闻时效性改进了基于用户的协同过滤算法. 最后进行了仿真实验, 实验结果表明, 该方法可以有效提高推荐算法的性能, 改善新闻推荐准确度和召回率.