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| 基于超图随机游走标签扩充的微博推荐方法 |
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马慧芳1,2, 张迪1, 赵卫中3, 史忠植4
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1.西北师范大学 计算机科学与工程学院, 甘肃 兰州 730070;2.广西可信软件重点实验室(桂林电子科技大学), 广西 桂林 541004;3.华中师范大学 计算机学院, 湖北 武汉 430079;4.中国科学院 计算技术研究所 智能信息处理重点实验室, 北京 100190
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| 摘要: |
| 向微博用户推荐对其有价值和感兴趣的内容，是改善用户体验的重要途径.通过分析微博特点以及现有微博推荐算法的缺陷，利用标签信息表征用户兴趣，提出一种结合标签扩充与标签概率相关性的微博推荐方法.首先，考虑到大部分微博用户未给自己添加任何标签或添加标签过少，视用户发布微博为超边，微博中的词视为超点来构建超图，并以一定的加权策略对超边和超点进行加权，通过在超图上随机游走，得到一定数量的关键词，对微博用户标签进行扩充；然后，采用相关性标签权重加权方案构建用户-标签矩阵，利用标签之间的概率相关性，构造标签相似性矩阵，对用户-标签矩阵进行更新，使该矩阵既包含用户兴趣信息，又包含标签与标签之间的关系.以新浪微博公开API抓取的微博信息作为实验数据进行了一系列的实验和分析，结果表明，该推荐算法具有较好的效果. |
| 关键词: 超图 随机游走 标签扩充 概率相关性 用户-标签矩阵 微博推荐 |
| DOI：10.13328/j.cnki.jos.005545 |
| 分类号:TP311 |
| 基金项目:国家自然科学基金（61762078，61363058，61762079）；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）；广西可信软件重点实验室研究课题（kx201910） |
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| Microblog Recommendation Method Based on Hypergraph Random Walk Tag Extension |
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MA Hui-Fang1,2, ZHANG Di1, ZHAO Wei-Zhong3, SHI Zhong-Zhi4
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1.College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China;2.Guilin University of Electronic Technology(Guangxi Key Laboratory of Trusted Software), Guilin 541004, China;3.School of Computer Science, Central China Normal University, Wuhan 430079, China;4.Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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| Abstract: |
| Recommending valuable and interesting contents for microblog users is an important way to improve the user experience. In this study, tags are considered as the users' interests and a microblog recommendation method based on hypergraph random walk tag Extension and tag probability correlation is proposed via the analysis of characteristics and the existing limitations of microblog recommendation algorithm. Firstly, microblogs are considered as hyperedges, while each term is taken as the hypervertex, and the weighting strategies for both hyperedges and hypervertexes are established. A random walk is conducted on the hypergraph to obtain a number of keywords for the expansion of microblog users. And then the weight of the tag for each user is enhanced based on the relevance weighting scheme and the user tag matrix can be constructed. Probability correlation between tags is calculated to construct the tag similarity matrix, which can be used to update the matrix is updated using the label similarity matrix, which contains both the user interest information and the relationship between tags and tags. Experimental results show that the algorithm is effective in microblog recommendation. |
| Key words: hypergraph random walk label expansion probability correlation user-tag matrix microblog recommendation |
