TY - JOUR
T1 - Level-based link analysis
AU - Feng, Guang
AU - Liu, Tie-Yan
AU - Zhang, Xu-Dong
AU - Qin, Tao
AU - Gao, Bin
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - In order to get high-quality web pages, search engines often resort retrieval pages by their ranks. The rank is a kind of measurement of importance of pages. Famous ranking algorithms, including PageRank and HITS, make use of hyperlinks to compute the importance. Those algorithms consider all hyperlinks identically in sense of recommendation. However, we find that the World Wide Web is actually organized with the natural multi-level structure. Benefiting from the level properties of pages, we can describe the recommendation of hyperlinks more reasonably and precisely. With this motivation, a new level-based link analysis algorithm is proposed in this paper. In the proposed algorithm, the recommendation weight of each hyperlink is computed with the level properties of its two endings. Experiments on the topic distillation task of TREC2003 web track show that our algorithm can evidently improve searching results as compared to previous link analysis methods. © Springer-Verlag Berlin Heidelberg 2005.
AB - In order to get high-quality web pages, search engines often resort retrieval pages by their ranks. The rank is a kind of measurement of importance of pages. Famous ranking algorithms, including PageRank and HITS, make use of hyperlinks to compute the importance. Those algorithms consider all hyperlinks identically in sense of recommendation. However, we find that the World Wide Web is actually organized with the natural multi-level structure. Benefiting from the level properties of pages, we can describe the recommendation of hyperlinks more reasonably and precisely. With this motivation, a new level-based link analysis algorithm is proposed in this paper. In the proposed algorithm, the recommendation weight of each hyperlink is computed with the level properties of its two endings. Experiments on the topic distillation task of TREC2003 web track show that our algorithm can evidently improve searching results as compared to previous link analysis methods. © Springer-Verlag Berlin Heidelberg 2005.
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U2 - 10.1007/978-3-540-31849-1_19
DO - 10.1007/978-3-540-31849-1_19
M3 - RGC 21 - Publication in refereed journal
SN - 0302-9743
VL - 3399
SP - 183
EP - 194
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
T2 - 7th Asia-Pacific Web Conference on Web Technologies Research and Development - APWeb 2005
Y2 - 29 March 2005 through 1 April 2005
ER -