TY - JOUR
T1 - Time-weighted web authoritative ranking
AU - Manaskasemsak, Bundit
AU - Rungsawang, Arnon
AU - Yamana, Hayato
N1 - Funding Information:
Acknowledgments This research is supported by the Thailand Research Fund through the Royal Golden Jubilee Ph.D. Program (Grant No. PHD/0122/2548). We thank the members of Yamana Laboratory for their help in data preparation and query evaluations. We also thank James Brucker for his valuable comments in the paper. In addition, we thank the anonymous reviewers for their comments and suggestions that have been incorporated into this article.
Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2011/4
Y1 - 2011/4
N2 - We investigate temporal factors in assessing the authoritativeness of web pages. We present three different metrics related to time: age, event, and trend. These metrics measure recentness, special event occurrence, and trend in revisions, respectively. An experimental dataset is created by crawling selected web pages for a period of several months. This data is used to compare page rankings by human users with rankings computed by the standard PageRank algorithm (which does not include temporal factors) and three algorithms that incorporate temporal factors, including the Time-Weighted PageRank (TWPR) algorithm introduced here. Analysis of the rankings shows that all three temporal-aware algorithms produce rankings more like those of human users than does the PageRank algorithm. Of these, the TWPR algorithm produces rankings most similar to human users', indicating that all three temporal factors are relevant in page ranking. In addition, analysis of parameter values used to weight the three temporal factors reveals that age factor has the most impact on page rankings, while trend and event factors have the second and the least impact. Proper weighting of the three factors in TWPR algorithm provides the best ranking results.
AB - We investigate temporal factors in assessing the authoritativeness of web pages. We present three different metrics related to time: age, event, and trend. These metrics measure recentness, special event occurrence, and trend in revisions, respectively. An experimental dataset is created by crawling selected web pages for a period of several months. This data is used to compare page rankings by human users with rankings computed by the standard PageRank algorithm (which does not include temporal factors) and three algorithms that incorporate temporal factors, including the Time-Weighted PageRank (TWPR) algorithm introduced here. Analysis of the rankings shows that all three temporal-aware algorithms produce rankings more like those of human users than does the PageRank algorithm. Of these, the TWPR algorithm produces rankings most similar to human users', indicating that all three temporal factors are relevant in page ranking. In addition, analysis of parameter values used to weight the three temporal factors reveals that age factor has the most impact on page rankings, while trend and event factors have the second and the least impact. Proper weighting of the three factors in TWPR algorithm provides the best ranking results.
KW - Link analysis
KW - PageRank
KW - Search engine
KW - Time-weighted ranking
KW - Web authoritativeness
KW - Web ranking algorithm
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U2 - 10.1007/s10791-010-9138-4
DO - 10.1007/s10791-010-9138-4
M3 - Article
AN - SCOPUS:79953186638
SN - 1386-4564
VL - 14
SP - 133
EP - 157
JO - Information Retrieval
JF - Information Retrieval
IS - 2
ER -