Ranking by community relevance

Authors:
Lan Nie;Brian D. Davison;Baoning Wu
Affiliations:
Lehigh University, Bethlehem, PA;Lehigh University, Bethlehem, PA;Lehigh University, Bethlehem, PA
Venue:
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2007

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Abstract

A web page may be relevant to multiple topics; even when nominally on a single topic, the page may attract attention (and thus links) from multiple communities. Instead of indiscriminately summing the authority provided by all pages, we decompose a web page into separate subnodes with respect to each community pointing to it. By considering the relevance of these communities, we are able to better model the query-specific reputation for each potential result. We apply a total of 125 queries to the TREC .GOV dataset to demonstrate how the use of community relevance can improve ranking performance.