HITS algorithm improvement using semantic text portion

Bui Quang Hung, Masanori Otsubo, Y. Hijikata, S. Nishida
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引用次数: 14

Abstract

Kleinberg's Hypertext-Induced Topic Selection (HITS) algorithm is a popular and effective algorithm to rank web pages. One of its problems is the topic drift problem. Previous researches have tried to solve this problem using anchor-related text. In this paper, we investigate the effectiveness of using Semantic Text Portion for improving the HITS algorithm. In detail, we examine the degree to which we can improve the HITS algorithm. We also compare STPs with other kinds of anchor-related text from the viewpoint of improving the HITS algorithm. The experimental results demonstrate that the use of STPs is best for improving the HITS algorithm.
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使用语义文本部分改进HITS算法
Kleinberg的超文本诱导主题选择(HITS)算法是一种流行且有效的网页排名算法。其中一个问题是主题漂移问题。以往的研究尝试使用锚相关文本来解决这一问题。在本文中,我们研究了使用语义文本部分改进HITS算法的有效性。详细地,我们检查了我们可以改进HITS算法的程度。我们还从改进HITS算法的角度将stp与其他类型的锚相关文本进行了比较。实验结果表明,stp是改进HITS算法的最佳方法。
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