Haveliwala, Taher H. (2002) Topic-Sensitive PageRank. In: Eleventh International World Wide Web Conference (WWW 2002), May 7-11, 2002, Honolulu, Hawaii.
In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate query-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||search, web graph, link structure, PageRank, search in context, personalized search|
|Subjects:||Computer Science > Databases and the Web|
|Related URLs:||Project Homepage, Project Homepage||http://infolab.stanford.edu/, http://infolab.stanford.edu/|
|Deposited By:||Import Account|
|Deposited On:||09 Feb 2002 16:00|
|Last Modified:||25 Dec 2008 09:29|
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