中文摘要：

针对传统推荐方法在短文本处理方面的不足，提出一种基于用户兴趣模型与会话抽取算法的微博推荐方法。该方法应用基于归一化割加权NMF的微博用户兴趣模型获取用户-主题矩阵，产生用户感兴趣的微博主题，结合基于Single-Pass聚类模型的会话在线抽取算法SPFC（single-passbasedonfrequencyandcorrelation）获取微博的会话队列，并与用户感兴趣的微博主题进行相似度计算，最后得到实时的微博推荐结果。实验表明，此方法能有效地进行微博推荐。

英文摘要：

Aiming at the deficiency of conventional recommendation method in short text message processing, this paper pro- posed a microblog recommendation method based on user interest model and conversation extraction. First, it applied a Ncut weighted non-negative matrix factorization （Ncut weighted NMF）to obtain user-interest matrix. And then used Single-Pass clustering based on frequency and correlation for conversation extraction to obtain mieroblog conversation. Experiments show that this method can effectively cluster micro-blogs and support micro-blog recommendation.