document: Beyond PageRank : machine learning for static ranking Since the publication of Brin and Page 's paper on PageRank , many in the Web community have depended on PageRank for the static ( query-independent ) ordering of Web pages . We show that we can significantly outperform PageRank using features that are independent of the link structure of the Web . We gain a further boost in accuracy by using data on the frequency at which users visit Web pages . We use RankNet , a ranking machine learning algorithm , to combine these and other static features based on anchor text and domain characteristics . The resulting model achieves a static ranking pairwise accuracy of 67.3 % ( vs. 56.7 % for PageRank or 50 % for random ) .
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