Mining web multi-resolution community-based popularity for information retrieval

Laurence A.F. Park, Kotagiri Ramamohanarao

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

13 Citations (Scopus)

Abstract

The PageRank algorithm is used in Web information retrieval to calculate a single list of popularity scores for each page in the Web. These popularity scores are used to rank query results when presented to the user. By using the structure of the entire Web to calculate one score per document, we are calculating a general popularity score, not particular to any community. Therefore, the PageRank scores are more suited to general queries. In this paper, we introduce a more general form of PageRank, using Web multi-resolution community-based popularity scores, where each document obtains a popularity score dependent on a given Web community. When a query is related to a specific community, we choose the associated set of popularity scores and order the query results accordingly. Using Web-community based popularity scores, we achieved an 11% increase in precision over PageRank.

Original languageEnglish
Title of host publicationCIKM 2007 - Proceedings of the 16th ACM Conference on Information and Knowledge Management
Pages545-554
Number of pages10
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event16th ACM Conference on Information and Knowledge Management, CIKM 2007 - Lisboa, Portugal
Duration: 6 Nov 20079 Nov 2007

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference16th ACM Conference on Information and Knowledge Management, CIKM 2007
Country/TerritoryPortugal
CityLisboa
Period6/11/079/11/07

Keywords

  • Pagerank
  • Symmetric non-negative matrix factorisation

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