Article

Adaptive ranking of web pages

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Marco MagginiAuthors Info & Claims

WWW '03: Proceedings of the 12th international conference on World Wide Web

Pages 356 - 365

https://doi.org/10.1145/775152.775203

Published: 20 May 2003 Publication History

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Abstract

In this paper, we consider the possibility of altering the PageRank of web pages, from an administrator's point of view, through the modification of the PageRank equation. It is shown that this problem can be solved using the traditional quadratic programming techniques. In addition, it is shown that the number of parameters can be reduced by clustering web pages together through simple clustering techniques. This problem can be formulated and solved using quadratic programming techniques. It is demonstrated experimentally on a relatively large web data set, viz., the WT10G, that it is possible to modify the PageRanks of the web pages through the proposed method using a set of linear constraints. It is also shown that the PageRank of other pages may be affected; and that the quality of the result depends on the clustering technique used. It is shown that our results compared well with those obtained by a HITS based method.

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Adaptive ranking of web pages

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Published In

WWW '03: Proceedings of the 12th international conference on World Wide Web

May 2003

772 pages

ISBN:1581136803

DOI:10.1145/775152

Conference Chairs:
Gusztáv Hencsey
MTA SZTAKI, Hungary
,
Bebo White
Stanford Linear Accelerator Center, USA
,
Program Chairs:
Yih-Farn Robin Chen
AT&T Labs -- Research, USA
,
László Kovács
MTA SZTAKI, Hungary
,
Steve Lawrence
Google Inc., USA

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Association for Computing Machinery

New York, NY, United States

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Published: 20 May 2003

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Abbas KHasan MAbbasi AMokhtar UKhan AAbdullah SDong SIslam SAlboaneen DAhmed F(2023)Predicting the Future Popularity of Academic Publications Using Deep Learning by Considering It as Temporal Citation NetworksIEEE Access10.1109/ACCESS.2023.329090611(83052-83068)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3290906
Yue HLiu HChen J(2022) A Gospel for MOBA Game: Ranking-Preserved Hero Change Prediction in Dota 2 IEEE Transactions on Games10.1109/TG.2021.312358314:2(191-201)Online publication date: Jun-2022
https://doi.org/10.1109/TG.2021.3123583
Adewole SFernandes PJablonski JCopland APorter MSyed SBrown D(2021)Graph Convolutional Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671281(388-399)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671281
Berend G(2020)Efficient algorithm to compute Markov transitional probabilities for a desired PageRankEPJ Data Science10.1140/epjds/s13688-020-00240-z9:1Online publication date: 29-Jul-2020
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Lai YHsu CChen WYeh MLin S(2019)DeepRankData Mining and Knowledge Discovery10.1007/s10618-018-0601-y33:2(474-498)Online publication date: 1-Mar-2019
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Song QZong BWu YTang LZhang HJiang GChen HCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)TGNetProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271698(97-106)Online publication date: 17-Oct-2018
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Hsu CLai YChen WFeng MLin Sde Rijke MShokouhi MTomkins AZhang M(2017)Unsupervised Ranking using Graph Structures and Node AttributesProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018668(771-779)Online publication date: 2-Feb-2017
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A Googol of Information about Google

Re-ranking search results using query logs

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