Tag and Topic Recommendation Systems

被引：0

作者：

Bogardi-Meszoely, Agnes ^{[1
,3
]}

Roevid, Andras ^{[2
]}

Ishikawa, Hiroshi ^{[3
]}

Yokoyama, Shohei ^{[3
]}

Vamossy, Zoltan ^{[2
]}

机构：

[1] Budapest Univ Technol & Econ, Dept Automat & Appl Informat, H-1117 Budapest, Hungary

[2] Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary

[3] Shizuoka Univ, Dept Comp Sci, Naka Ku, Hamamatsu, Shizuoka 4328011, Japan

来源：

ACTA POLYTECHNICA HUNGARICA | 2013年 / 10卷 / 06期

关键词：

social network; tag cloud; tag analysis; vocabulary; reference count; font distribution algorithm; topic recommendation; CLOUD;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The spread of Web 2.0 has caused user-generated content explosion. Users can tag resources in order to describe and organize them. A tag cloud provides rough impression of relative importance of each tag within the overall cloud in order to facilitate browsing among numerous tags and resources. The size of its vocabulary may be huge, moreover, it is incomplete and inconsistent. Thus, the goal of our paper is to establish tag and topic recommendation systems. Firstly, for tag recommendation system novel algorithms have been proposed to refine vocabulary, enhance reference counts, and improve font distribution for enriched visualization. Secondly, for topic recommendation system novel algorithms have been provided to construct a special graph from tags and evaluate reference counts for topic identification. The proposed recommendation systems have been validated and verified on the tag cloud of a real-world thesis portal.

引用

页码：171 / 191

页数：21

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