HITS algorithm improvement using semantic text portion

Authors:
Bui Quang Hung;Masanori Otsubo;Yoshinori Hijikata;Shogo Nishida
Affiliations:
Correspd. E-mail: bqhung@nishilab.sys.es.osaka-u.ac.jp;-;-;Graduate School of Engineering Science, Osaka University, Osaka 560-6531, Japan
Venue:
Web Intelligence and Agent Systems
Year:
2010

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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.