document: Topic-sensitive PageRank 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 .
doc_bio_tags: O B O O O B O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O