中文摘要：

随着社交网络的发展,人们在这个＂信息爆炸＂的时代,却有了＂信息饥渴＂的困惑。新浪微博用户目前面临的主要问题有两个：一是若不及时查看微博,则用户想看的有关话题的博文将被排到了最后,很可能被忽略;二是若用户对其中的某一博文所讨论的话题感兴趣,则会想得到更多关于该话题的信息。文章首先使用K-means文本聚类算法提取出用户关注的话题,使用因子分析法进行指标分析,构建用户影响力和博文影响力模型,再通过RS分值排名法和线性回归法确定用于博文影响力和用户影响力的参数值,最后提出了基于K-means文本聚类算法的个性化新浪博文及时推荐模型。通过实验验证,文章提出的方法能很有效地解决个性化博文及时推荐问题。

英文摘要：

With the development of social networks, people suffering from the ＂information hungry＂ confusion in this era of ＂information explosion＂. Sina microblog users have two problems： First, if the user can＇t view tweets on microblog timely,the tweet about a topic which the user interested in will be pushed to the end and be ignored; Second, if the user is interest-ed in a topic which be mentioned by a tweet, he/she may want to get more information about this topic.Firstly, the paper usethe K-cores analysis method to extract topics which the user is interested in,and use the method of factor analysis to analy-sis index, extracts the tweet heat factor and user authority factor; then, use the method of the RS and the linear regression todetermine the parameters for balance, the value of the tweet heat factor and user authority factor. Finally, This paper propos-es the personalized recommendation model based on k-means text clustering algorithm for Sina tweets timely.According tothe experimental results, the proposed method in this paper can effectively solve the problem of microblog timely personal-ized recommendation.