Latent Dirichlet Allocation(LDA) model can be used for identifying topic information from large-scale document set, but the effect is not ideal for short text such as microblog. This paper proposes a microblog user model based on LDA, which divides microblog based on user and represents each user with their posted microbolgs. Thus, the standard three layers in LDA model by document-topic-word becomes a user model by user-topic-word. The model is applied to user recommendation. Experiment on real data set shows that the new provided method has a better effect. With a proper topic number, the performance is improved by nearly 10%.%潜在狄利克雷分配(LDA)主题模型可用于识别大规模文档集中潜藏的主题信息,但是对于微博短文本的应用效果并不理想。为此,提出一种基于LDA的微博用户模型,将微博基于用户进行划分,合并每个用户发布的微博以代表用户,标准的文档-主题-词的三层LDA模型变为用户-主题-词的用户模型,利用该模型进行用户推荐。在真实微博数据集上的实验结果表明,与传统的向量空间模型方法相比,采用该方法进行用户推荐具有更好的效果,在选择合适的主题数情况下,其准确率提高近10%。
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机译:Litchfield心理咨询模型以哲学为基础,将精神健康服务应用程序Lift Up UP,旨在提供简单,实用的建议,以帮助个人和员工应对日常的精神健康挑战,并将用户与现有的精神健康专业人员联系起来。 Lift me UP将使用先进的技术来:•协助患者评估过程•监控和支持日常工作•将用户推荐给可用的心理健康专家•与市场上的任何产品相比,创造独特的定制体验。