Tag and Topic Recommendation Systems
Tag and Topic Recommendation Systems
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DOI：
10.12700/aph.10.06.2013.6.10
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Ágnes Bogárdi-Mészöly;A. Rövid;H. Ishikawa;Shohei Yokoyama;Z. Vámossy
中科院分区：
文献类型：
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作者：
Ágnes Bogárdi-Mészöly;A. Rövid;H. Ishikawa;Shohei Yokoyama;Z. Vámossy
Web 2.0的普及导致了用户生成内容的爆炸式增长。用户可以标记资源，以便描述和组织它们。标签云提供了每个标签在整个云中的相对重要性的粗略印象，以便于在众多标签和资源之间进行浏览。它的词汇量可能是巨大的，而且，它是不完整和不一致的。因此，本文的目标是建立标签和主题推荐系统。首先，对于标签推荐系统，已经提出了新的算法来细化词汇，提高引用计数，并改善字体分布以丰富可视化。其次，对于主题推荐系统，提出了一种新的算法来构造一个特殊的标签图，并评估主题识别的引用计数。建议的推荐系统已被验证和验证的标签云的真实世界的论文门户网站。
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.