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Recent web search techniques augment traditional text matching with a global notion of "importance" based on the linkage structure of the web, such as in Google's PageRank algorithm. For more refined searches, this global notion of importance can be specialized to create personalized views of importance--for example, importance scores can be biased(More)
  • Taher Haveliwala, Sepandar Kamvar, Glen Jeh
  • 2003
PageRank, the popular link-analysis algorithm for ranking web pages, assigns a query and user independent estimate of " importance " to web pages. Query and user sensitive extensions of PageRank, which use a basis set of biased PageRank vectors, have been proposed in order to personalize the ranking function in a tractable way. We analytically compare three(More)
Existing data mining algorithms on graphs look for nodes satisfying specific properties, such as specific notions of structural similarity or specific measures of link-based importance. While such analyses for predetermined properties can be effective in well-understood domains, sometimes identifying an appropriate property for analysis can be a challenge,(More)
Major search engines currently use the history of a user's actions (e.g., queries, clicks) to personalize search results. In this paper, we present a new personalized service, <i>query-specific web recommendations</i> (QSRs), that retroactively answers queries from a user's history as new results arise. The QSR system addresses two important subproblems(More)
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