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基于词项关联关系与归一化割加权的微博用户兴趣模型

Microblog user interest modeling based on term correlation and normalized cut weighting scheme
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摘要 提出一种基于词项关联关系与归一化割加权非负矩阵分解的微博用户兴趣模型构建方法。该方法首先基于词分布上下文语义相关性来建立词项关联关系矩阵刻画词项间相似度,然后应用归一化割加权非负矩阵分解算法获取用户—主题矩阵,产生用户感兴趣的微博主题聚类结果。实验表明,此方法能有效地进行微博主题聚类,并支持微博用户兴趣模型构建。 This paper proposed a non-negative matrix factorization based on the term correlation and normalized cut weighting for miero-blog user interest model. First, it constructed a term correlation matrix using term distribution context to better ex- plain similarities of terms, and then presented a Ncut-weighted non-negative matrix factorization （ NCUT_WEIGHTED NMF） method to obtain the matrix of user-topic ,which showed the clustering results of interest to the user. Experiments show that this method can effectively cluster micro-blog topic to support miero-blog user interest model.
作者 姚伟 马慧芳 孙曰昕 张志昌
机构地区 西北师范大学计算机科学与工程学院
出处 《计算机应用研究》 CSCD 北大核心 2015年第6期1630-1633,共4页 Application Research of Computers
基金 国家自然科学基金资助项目(61163039 61363058) 甘肃省教育厅资助项目(2013A-016) 甘肃省青年科技基金(145RJYA59) 中国科学院计算技术研究所智能信息处理重点实验室开放基金(IIP2014-4)
关键词 词关联关系矩阵 归一化割 非负矩阵分解 微博用户兴趣模型 term correlation matrix normalized cut nonnegative matrix factorization microblog user interest model
分类号 TP391.1 [自动化与计算机技术—计算机应用技术]
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参考文献12

  • 1陈文涛,张小明,李舟军.构建微博用户兴趣模型的主题模型的分析[J].计算机科学,2013,40(4):127-130. 被引量：30
  • 2Yan Xiaohui,Guo Jiafeng,Liu Shenghua,et al.Clustering short text using Ncut-weighted non-negative matrix factorization[C]//Proc of the 21st ACM International Conference on Information and Knowledge Management.New York:ACM Press,2012:2259-2262.
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二级参考文献18

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计算机应用研究
2015年 第6期

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