loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Hung-Yu Kao 1 ; Chia-Sheng Liu 1 ; 1 ; Chia-Chun Shih 2 and Tse-Ming Tse-Ming 2

Affiliations: 1 National Cheng Kung University, Taiwan ; 2 Innovative Digitech-Enabled Applications & Services Institute (IDEAS), Institute for Information Industry, Taiwan

Keyword(s): Search engine, Link Analysis, PageRank, Web Graph, Hierarchical Structure, Page Quality.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Searching and Browsing ; Soft Computing ; Symbolic Systems ; Web Information Systems and Technologies ; Web Interfaces and Applications ; Web Mining

Abstract: 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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.239.15.46

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Kao, H.; Liu, C.; Tsai, Y.; Shih, C. and Tse-Ming, T. (2008). DRANK+: A DIRECTORY BASED PAGERANK PREDICTION METHOD FOR FAST PAGERANK CONVERGENCE. In Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-8111-27-2; ISSN 2184-3252, SciTePress, pages 175-180. DOI: 10.5220/0001521701750180

@conference{webist08,
author={Hung{-}Yu Kao. and Chia{-}Sheng Liu. and Yu{-}Chuan Tsai. and Chia{-}Chun Shih. and Tse{-}Ming Tse{-}Ming.},
title={DRANK+: A DIRECTORY BASED PAGERANK PREDICTION METHOD FOR FAST PAGERANK CONVERGENCE},
booktitle={Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2008},
pages={175-180},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001521701750180},
isbn={978-989-8111-27-2},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - DRANK+: A DIRECTORY BASED PAGERANK PREDICTION METHOD FOR FAST PAGERANK CONVERGENCE
SN - 978-989-8111-27-2
IS - 2184-3252
AU - Kao, H.
AU - Liu, C.
AU - Tsai, Y.
AU - Shih, C.
AU - Tse-Ming, T.
PY - 2008
SP - 175
EP - 180
DO - 10.5220/0001521701750180
PB - SciTePress