基于信任关系和词相关关系的冷启动用户词特征重建

摘要

The user s' word feature obtained from the text is the basis for achieving the task of user theme modeling , interest mining ,and personalized recommendation .To derive the word feature for cold start users who contain scarcely texts ,this paper presents a method of merging the trust relations of the user and the word correlation ,Spe-cifically ,we combine the user s' trust relation matrix ,words correlation matrix and the feature word matrix via prob-abilistic matrix factorization .The experimental results on 4 data sets from Sina microblogging and twitter show that the proposed algorithm achieves better results .%文本是社交媒体用户的重要信息之一,从文本中获取用户的词特征是实现用户主题建模、兴趣挖掘及个性化推荐等任务的基础.然而社交媒体中存在许多用户(冷启动用户)只含有少量甚至缺乏文本信息,为此该文提出一种融合用户信任关系及词相关关系的词特征重建方法.该方法通过对用户信任关系矩阵、词相关关系矩阵和用户词特征矩阵进行联合概率矩阵分解来实现对冷启动用户的词特征重建.在新浪微博和Twitter的四组数据集上的实验结果表明,该文所提出的冷启动用户词特征重建算法能够取得较好的词特征重建结果.