由于新闻更新快，对用户进行新闻推荐往往需要进行聚类预处理，而传统方法要么复杂度过高，要么依赖于迭代初值，都不能准确而高效地应用于新闻推荐中．针对以上问题，文中提出了一个基于二次聚类的新闻推荐方法，对随机抽样数据进行密度聚类，基于该样本密度聚类的簇数和初始簇心进行所有待推荐新闻的二次快速聚类，并结合时新性、新闻热度等因素实现新闻推荐．文中方法可以将相关新闻聚集在一起，同时又不导致过高的运算开销，并通过参数估计方法计算各因素参数．实验结果表明，与其他新闻推荐方法相比，文中方法具有较好的推荐多样性和推荐准确度．

Due to fast update of news,the clustering- based preprocessing is usually needed when the news is recom-mended to users.However,some traditional clustering methods are too complicated while others rely on iterative ini-tial value,none of which can be accurately and effectively applied to news recommendation.Considering the aboveissues,we propose a news recommendation method based on two- fold clustering.In this method,a density clusteringof random sample data is conducted.Based on the cluster number and initial cluster center of the density clustering,a fast two- fold clustering of all the news to be recommended is performed.Then,the news recommendation is realizedby combining such factors as fashionability and popularity.The proposed method can cluster relevant news without toomuch computation cost,and it can calculate parameters by means of parameter estimation.Experimental results showthat the proposed method is superior to other news recommendation methods in terms of diversity and accuracy.