Beyond PageRank: Machine Learning for Static Ranking. Richardson, M.; Prakash, A.; and Brill, E. In Proceedings of the 15th International Conference on World Wide Web, of WWW '06, pages 707–715, New York, NY, USA, 2006. ACM.
Beyond PageRank: Machine Learning for Static Ranking [link]Paper  doi  abstract   bibtex   
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).
@inproceedings{richardson_beyond_2006,
	address = {New York, NY, USA},
	series = {{WWW} '06},
	title = {Beyond {PageRank}: {Machine} {Learning} for {Static} {Ranking}},
	isbn = {978-1-59593-323-2},
	shorttitle = {Beyond {PageRank}},
	url = {http://doi.acm.org/10.1145/1135777.1135881},
	doi = {10.1145/1135777.1135881},
	abstract = {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).},
	urldate = {2017-04-28},
	booktitle = {Proceedings of the 15th {International} {Conference} on {World} {Wide} {Web}},
	publisher = {ACM},
	author = {Richardson, Matthew and Prakash, Amit and Brill, Eric},
	year = {2006},
	keywords = {PageRank, RankNet, relevance, search engines, static ranking},
	pages = {707--715}
}
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