中文摘要：

针对贴吧用户面临严重的信息过载问题，提出一种基于用户信息的协同过滤帖子推荐方法。分析帖子推荐的属性特点后，首先利用一个融合了用户评论行为的PageRank算法去判断参与一个帖子讨论中各用户的重要性，主要考虑各用户之间的回复关系以及各用户之间回复的时间关系；然后把PageRank得分高的用户作为聚类中心进行 k-means聚类；最后把帖子中聚类得到的用户与推荐系统使用者通过协同过滤算法计算相似度，并结合用户的PageRank得分，选择与用户相关度较高的帖子作为推荐结果。实验结果表明，该模型比现在使用的热门帖子推荐有着更好的表现。

英文摘要：

In order to solve the problem of information overload in the post bar ,a method of information filtration was proposed based on the user＇s commenting behavior .After analyzing the properties of the recommended posts ,the importance of an individual user was evaluated by the PageRank algorithm ,in which the weight of replies to the posts among users and the weight of reply intervals were taken into consideration .The users with a high PageRank score were then taken as a cluster center in k-means clustering .The similarity between two groups of users （one from the clustering analysis and the other from the recommending system ） was calculated by a collaborative filtering algorithm .The posts with high correlations to the users were presented as the recommended results .Experimental results show that the proposed method performs better than the recommending methods in use .