基于标签和PageRank的重要微博用户推荐算法

Important Micro-blog User Recommendation Algorithm Based on Label and PageRank

海量的微博信息使新进用户很难获取到其感兴趣的内容,重要微博用户推荐为新用户提供了一条有效获取信息的途径。目前,由于用户间的关系没有被充分考虑及缺乏对用户个性化标签的处理,导致重要微博用户推荐的准确率不高。为此,提出了一种基于标签和PageRank的重要微博用户推荐算法。该算法首先对个性化标签进行分词、去噪、设置权重等处理,并将其作为用户兴趣的代表;然后根据PageRank计算模型来分析用户间的关系,结合标签相似度计算向新用户推荐与其兴趣相似的重要微博用户。实验表明,该算法由于融入了对微博用户关系和用户个性化标签的重要性分析,因此与基于标签和协同过滤的个性化推荐算法相比具有更高的重要微博用户推荐准确率。

Massive micro-blog information makes it difficult for new users to obtain the content they are interested in.Important micro-blog user recommendation provides an effective way for new users to access information.At present,inadequate consideration of the relationship between users and the lack of user personalized label processing make the recommendation accuracy of important micro-blog user be not high.Therefore,an important micro-blog user recommendation algorithm based on label and PageRank was proposed.Firstly,the personalized label is processed by word segmentation,de-noising and setting weight,and the processed result is used as the representative of user interest.Secondly,the relationship between users is analyzed by PageRank calculation model.Finally,important micro-blog users are recommended to new users with similar interests by label similarity calculation.The experiment shows that the proposed algorithm improves the recommendation accuracy of important micro-blog users compared with the recommendation algorithm based on label and collaborative filtering,because the analysis of the importance of micro-blog user relationship and user＇s personalized label is integrated into this algorithm.