TY - GEN
T1 - DRank+
T2 - WEBIST 2008 - 4th International Conference on Web Information Systems and Technologies
AU - Kao, Hung Yu
AU - Liu, Chia Sheng
AU - Tsai, Yu Chuan
AU - Shih, Chia Chun
AU - Tsai, Tse Ming
PY - 2008/12/1
Y1 - 2008/12/1
N2 - In recent years, most part of search engines use link analysis algorithms to measure the importance of web pages. The most famous link analysis algorithm is PageRank algorithm. However, previous researches in recent years have found that there exists an inherent bias against newly created pages in PageRank. In the previous work, a new ranking algorithm called DRank has been proposed to solve this issue. It utilizes the cluster phenomenon of PageRank in a directory to predict the possible importance of pages in the future and to diminish the inherent bias of search engines to new pages. In this paper, we modify the original DRank algorithm to complement the weaker part of DRank which could fail while the number of pages in directory is not enough. In our experiments, the augmented algorithm, i.e., DRank+ algorithm, obtains more accuracy in predicting the importance score of pages at next time stage than the original DRank algorithm. DRank+ not only alleviates the bias of newly created pages successfully but also reaches more accuracy than Page Quality and original DRank in predicting the importance of newly created pages.
AB - In recent years, most part of search engines use link analysis algorithms to measure the importance of web pages. The most famous link analysis algorithm is PageRank algorithm. However, previous researches in recent years have found that there exists an inherent bias against newly created pages in PageRank. In the previous work, a new ranking algorithm called DRank has been proposed to solve this issue. It utilizes the cluster phenomenon of PageRank in a directory to predict the possible importance of pages in the future and to diminish the inherent bias of search engines to new pages. In this paper, we modify the original DRank algorithm to complement the weaker part of DRank which could fail while the number of pages in directory is not enough. In our experiments, the augmented algorithm, i.e., DRank+ algorithm, obtains more accuracy in predicting the importance score of pages at next time stage than the original DRank algorithm. DRank+ not only alleviates the bias of newly created pages successfully but also reaches more accuracy than Page Quality and original DRank in predicting the importance of newly created pages.
UR - http://www.scopus.com/inward/record.url?scp=58049175923&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58049175923&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:58049175923
SN - 9789898111265
T3 - WEBIST 2008 - 4th International Conference on Web Information Systems and Technologies, Proceedings
SP - 175
EP - 180
BT - WEBIST 2008 - 4th International Conference on Web Information Systems and Technologies, Proceedings
Y2 - 4 May 2008 through 7 May 2008
ER -