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Topic-Sensitive PageRank. Taher H. Haveliwala 2002. Abstract. Target ： improving the ranking of search-query results B efore ： using the link structure of the Web, to capture the relative importance of Web pages, independent of any particular search query
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Topic-Sensitive PageRank Taher H. Haveliwala 2002
Abstract • Target：improvingthe ranking of search-query results • Before：using the link structure of the Web, to capture the relative importance of Web pages, independent of any particular search query • Now：a set of PageRank vectors, biased using a set of topics, to capture more accurately the notion of importance with respect to a particular topic
Abstract : contribution • more accurate rankings than generic PageRank • Compute topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords • Considering searches done in context • Compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared
1. Introduction • HITS [14] • a link analysis algorithm • Hubs • Authorities • Include content analyst [4] • Automatically compiling resource lists for general topics [8]
1. Introduction - PageRank algorithm[7,16] • rank vector - apriori importance -> estimate pages on the Web • Computed once • Offline • independent of the search query(con) • importance scores are used in conjunction with query-specific IR scores to rank the query results
1. Introduction - Advantage of PageRank • query-time cost of incorporating the precomputed PageRank importance score for a page is low • PageRank is generated using the entire Webgraphrather than a small subset
1. Introduction - Method in this paper • allows the query to influence the link-based score(HITS) • requires minimal query-time processing (PageRank) • biased with a different topic
1. Introduction -making PageRank topic-sensitive • avoid the problem of heavily linked pages getting highly ranked for queries(no particular authority) • Hilltop[5] that is designedto improve results for popular queries • Generates a query-specific authority score by detecting and indexing pages that appear to be good experts for certain keywords • experts were not found will not be handled by the Hilltop algorithm.
1. Introduction -making PageRank topic-sensitive • [17]Propose using a set of Web Pages terms for influencing the computation. • An approach for enhancing searchrankings by generating a PageRank vector for each possiblequery term was recently proposed in [18] with favorable results • requires considerable processing time and storage • not easily extended to makeuse of user and query context
1. Introduction - two query scenarios • Scenarios1：assume a user with a specific information need issues a query • Determine the topics most closely associated with the query, and use the appropriate topic-sensitive PageRank vectors for ranking the documents satisfying the query.
1. Introduction - two query scenarios • Scenario2：user is viewing a document (for instance, browsing the Web or reading email), and selects a term from the document for which he would like more information.
Summary of approach • generate 16 topic-sensitive PageRank vectors using URLs from a top-level category from the Open Directory Project (ODP) • At query time, calculate the similarityof the query to each topics • take the linear combination of the topic-sensitive vectors, weighted using the similarities of the query to the topics • link-based computations are performed offline, the query-time costs are not much
2. Review of PageRank • page u link to page v • Example • Yahoo -> important page(many pages point to it) • pointed to from Yahoo! are probably important • Nu -> outdegree of page u • Rank(p) importance of page p • link (u ,v)confersunits of rank to v
N is thenumber of pages, assign all pages the initial value 1/N • Bv represent the set of pages pointing to v • The final vector • contains the PageRank vector over the Web • computed only once after each crawl of the Web
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