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Beyond PageRank: machine learning for static ranking

9 years 10 months ago
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). Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning. H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval. General Terms Algorithms, Measurement, Performance, Experimentation. Keywords Static ranking, search engines, PageRank, RankNet, relevance
Matthew Richardson, Amit Prakash, Eric Brill
Added 22 Nov 2009
Updated 22 Nov 2009
Type Conference
Year 2006
Where WWW
Authors Matthew Richardson, Amit Prakash, Eric Brill
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