Microblog Recommendation Method Based on Hypergraph Random Walk Tag Extension

被引：0
|
作者：
Ma H.-F. [1 ,2 ]
Zhang D. [1 ]
Zhao W.-Z. [3 ]
Shi Z.-Z. [4 ]
机构：
[1] College of Computer Science and Engineering, Northwest Normal University, Lanzhou
[2] Guilin University of Electronic Technology (Guangxi Key Laboratory of Trusted Software), Guilin
[3] School of Computer Science, Central China Normal University, Wuhan
[4] Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing
来源：
Ruan Jian Xue Bao/Journal of Software | 2019年 / 30卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Hypergraph; Label expansion; Microblog recommendation; Probability correlation; Random walk; User-tag matrix;
D O I：
10.13328/j.cnki.jos.005545
中图分类号：
学科分类号：
摘要：
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. © Copyright 2019, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：3397 / 3412
页数：15
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