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Abstract:
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.
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