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| Category | Value | ||
| Available via | http://dbpubs.stanford.edu/pub/2002-6 | ||
| Next version(s) | 2003-29 | ||
| Submitted on | 10th of February 2002 | ||
| Author | Haveliwala, Taher H. | ||
| Title | Topic-Sensitive PageRank | ||
| Date of publication | 2002 | ||
| Published in | To appear in WWW-2002 | ||
| Citation | Taher H. Haveliwala. Topic-Sensitive PageRank, To appear in Proceedings of the Eleventh International World Wide Web Conference, 2002. | ||
| Number of pages | 11 | ||
| Language | English | ||
| Project | Stanford InfoLab; Database Group | ||
| Type | Conference or Journal Paper | ||
| Subject group | Databases and the Web | ||
| Abstract | 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. | ||
| Keywords | search, web graph, link structure, PageRank, search in context, personalized search | ||
| Sponsored by | Supported by NSF Grant IIS-0085896 | ||
| Fulltext source |
| Management of the document by | siroker@db.stanford.edu
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