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The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank

by: Matthew Richardson, and Pedro Domingos

In: Advances in Neural Information Processing Systems, Vol. 14 (2002) , p. 1441-1448.

Abstract

The PageRank algorithm, used in the Google search engine, greatly improves the results of Web search by taking into account the link structure of the Web. PageRank assigns to a page a score proportional to the number of times a random surfer would visit that page, if it surfed indefinitely from page to page, following all outlinks from a page with equal probability. We propose to improve PageRank by using a more intelligent surfer, one that is guided by a probabilistic model of the relevance of a page to a query. Efficient execution of our algorithm at query time is made possible by precomputing at crawl time and thus once for all queries the necessary terms. Experiments on two large subsets of the Web indicate that our algorithm significantly outperforms PageRank in the human-rated quality of the pages returned, while remaining efficient enough to be used in today’s large search engines.

BibTeX record

@article{Richardson:2002,
  abstract = {The PageRank algorithm, used in the Google search engine, greatly improves the results of Web search by taking into account the link structure of the Web. PageRank assigns to a page a score proportional to the number of times a random surfer would visit that page, if it surfed indefinitely from page to page, following all outlinks from a page with equal probability. We propose to improve PageRank by using a more intelligent surfer, one that is guided by a probabilistic model of the relevance of a page to a query. Efficient execution of our algorithm at query time is made possible by precomputing at crawl time (and thus once for all queries) the necessary terms. Experiments on two large subsets of the Web indicate that our algorithm significantly outperforms PageRank in the (human-rated) quality of the pages returned, while remaining efficient enough to be used in today’s large search engines.},
  added-at = {2007-12-14T02:45:44.000+0100},
  author = {Richardson, Matthew and Domingos, Pedro},
  biburl = {http://www.bibsonomy.org/bibtex/23f5fcd345dcf7f048f8be4f31d476300/diego_ma},
  interhash = {e9ea07baa8cea5e1049921b2d2c16266},
  intrahash = {3f5fcd345dcf7f048f8be4f31d476300},
  journal = {Advances in Neural Information Processing Systems},
  keywords = {inf_retrieval web},
  pages = {1441-1448},
  timestamp = {2007-12-14T02:45:44.000+0100},
  title = {The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank},
  volume = 14,
  year = 2002
}

Endnote record

%0 Journal Article
%1 Richardson:2002
%A Richardson, Matthew
%A Domingos, Pedro
%D 2002
%J Advances in Neural Information Processing Systems
%K 
%P 1441-1448
%T The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank
%V 14
%X The PageRank algorithm, used in the Google search engine, greatly improves the results of Web search by taking into account the link structure of the Web. PageRank assigns to a page a score proportional to the number of times a random surfer would visit that page, if it surfed indefinitely from page to page, following all outlinks from a page with equal probability. We propose to improve PageRank by using a more intelligent surfer, one that is guided by a probabilistic model of the relevance of a page to a query. Efficient execution of our algorithm at query time is made possible by precomputing at crawl time (and thus once for all queries) the necessary terms. Experiments on two large subsets of the Web indicate that our algorithm significantly outperforms PageRank in the (human-rated) quality of the pages returned, while remaining efficient enough to be used in today’s large search engines.