传统微博用户推荐算法采用的数据来源单一,模型简单,导致推荐准确率不高.针对这一问题,本文提出一种基于标签的User Profile推荐算法,根据微博数据的特点,深入研究“微博文本”、“标签”、“社交关系”和“用户自身基本信息”等因素对微博个性化推荐的影响,通过训练LDA主题模型和SVM分类器将它们转换为标签,并赋予权重来描述用户兴趣,进行用户推荐以提高推荐准确性.实验结果表明,与传统VSM模型方法相比,该算法进行用户推荐效果更佳.%Using of single data source and the simple model in the traditional micro-blog recommendation results in the low recommendation accuracy.Therefore,a new recommendation algorithm based on labeled User Profile is proposed to overcome such issue.By analyzing the significance and correlation of individual user data,such as text,label,social relationship,and personal information,the algorithm generates new labels and suggests related interests by training LDA model and SVM classifier.The user's interests are assigned by weighted sum of these factors.The overall recommendation accuracy is improved.The experiments show that the properties of the algorithm are better than the traditional VSM model,allowing users to have a better micro-blog experience.
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