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PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results. It is named after both the term "web page" and co-founder Larry ...
Google PageRank (Google PR) is one of the methods Google uses to determine a page's relevance or importance. Important pages receive a higher PageRank and are more likely to appear at the top of the search results. Google PageRank (PR) is a measure from 0 - 10. Google PageRank is based on backlinks.
Ranking of query is one of the fundamental problems in information retrieval (IR), [1] the scientific/engineering discipline behind search engines. [2] Given a query q and a collection D of documents that match the query, the problem is to rank, that is, sort, the documents in D according to some criterion so that the "best" results appear early in the result list displayed to the user.
Google PageSpeed is a family of tools by Google, Inc. [1] designed to help optimize website performance. [2] It was introduced at a Developer Conference in 2010. [3] [4] There are four main components of PageSpeed family tools:
Fig.1. Google matrix of Wikipedia articles network, written in the bases of PageRank index; fragment of top 200 X 200 matrix elements is shown, total size N=3282257 (from [1]) A Google matrix is a particular stochastic matrix that is used by Google's PageRank algorithm. The matrix represents a graph with edges representing links between pages.
Some predicates may be based on simple, deterministic and surface properties. For example, a crawler's mission may be to crawl pages from only the .jp domain. Other predicates may be softer or comparative, e.g., "crawl pages about baseball", or "crawl pages with large PageRank". An important page property pertains to topics, leading to 'topical ...
Google PageRank, link analysis algorithm; Webometrics Ranking of World Universities, Ranking Web of World Universities This page was last edited on 18 ...
A possible predictor is the anchor text of links; this was the approach taken by Pinkerton [23] in the first web crawler of the early days of the Web. Diligenti et al. [ 24 ] propose using the complete content of the pages already visited to infer the similarity between the driving query and the pages that have not been visited yet.
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