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 propor- tional 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 Page- Rank 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 pre- computing at crawl time (and thus once for all queries) the neces- sary terms. Experiments on two large subsets of the Web indicate that our algorithm significantly outperforms PageRank in the (hu- man-rated) quality of the pages returned, while remaining efficient enough to be used in today’s large search engines.