基于信息内容和用户关系的用户兴趣分类

Classifying interests of users based on information content and user relationship

从微博的内容属性和社交网络的信息传播规律特征出发,将微博文本与用户关注关系结合作为用户兴趣分类的标准,从而使提取的用户兴趣的更加准确、有效。借助建立的用户兴趣分类模型解决用户兴趣分类问题,选取新浪微博作为研究对象,应用LDA算法进行主题提取,应用LibSVM算法进行分类。实验证明,该方法分类时增加了对用户信息的全面性应用,而且与其他方法相比有更高的分类准确率。

Based on the content attributes of Weibo and the characteristics of the information dissemination law of social network,the paper combines the Weibo text with the user＇s follower relationship as the standard of user interest classification,so that the user＇s interest is more accurate and effective.Using the established user interest classification model to solve the problem of user interest classification,the paper chooses Sina Weibo as the research object,in which the main topic extraction algorithm is LDA,classification algorithm is LIBSVM.The experimental results show that the method can be used to classify the user information comprehensively and has higher classification accuracy than other methods.