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

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

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Conference on Information and Knowledge Management archive
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management table of contents

Lisbon, Portugal

SESSION: Web retrieval II (IR) table of contents

Pages 545-554

Year of Publication: 2007

ISBN:978-1-59593-803-9

Authors

Laurence A. F. Park	The University of Melbourne, Melbourne, Australia
Kotagiri Ramamohanarao	The University of Melbourne, Melbourne, Australia

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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