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

Authors:
Laurence A. F. Park;Kotagiri Ramamohanarao
Affiliations:
The University of Melbourne, Melbourne, Australia;The University of Melbourne, Melbourne, Australia
Venue:
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Year:
2007

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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.