Experimental bounds on the usefulness of personalized and topic-sensitive PageRank

Sinan Al-Saffar, Gregory Heileman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

PageRank is an algorithm used by several search engines to rank web documents according to their assumed relevance and popularity deduced from the Web's link structure. PageRank determines a global ordering of candidate search results according to each page's popularity as determined by the number and importance of pages linking to these results. Personalized and topic-sensitive PageRank are variants of the algorithm that return a local ranking based on each user's preference s as biased by a set of pages they trust or topics they prefer. In this paper we compare personalized and topic-sensitive local PageRanks to the global PageRank showing experimentally how similar or dissimilar results of personalization can be to the original global rank results and to other personalizations. Our approach is to examine a snapshot of the Web and determine how advantageous personalization can be in the best and worst cases and how it performs at various values of the damping factor in the PageRank formula.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
Pages671-675
Number of pages5
DOIs
StatePublished - 2007
Externally publishedYes
EventIEEE/WIC/ACM International Conference on Web Intelligence, WI 2007 - Silicon Valley, CA, United States
Duration: Nov 2 2007Nov 5 2007

Publication series

NameProceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007

Conference

ConferenceIEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
Country/TerritoryUnited States
CitySilicon Valley, CA
Period11/2/0711/5/07

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications

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