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Beyond PageRank: machine learning for static ranking

Published: 23 May 2006 Publication History

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).

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      cover image ACM Conferences
      WWW '06: Proceedings of the 15th international conference on World Wide Web
      May 2006
      1102 pages
      ISBN:1595933239
      DOI:10.1145/1135777
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      Published: 23 May 2006

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      Author Tags

      1. PageRank
      2. RankNet
      3. relevance
      4. search engines
      5. static ranking

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