Beyond PageRank: machine learning for static ranking

  • Authors:

  • Matthew Richardson;Amit Prakash;Eric Brill

  • Affiliations:

  • Microsoft Research, Redmond, WA;MSN, Redmond, WA;Microsoft Research, Redmond, WA

  • Venue:
  • Proceedings of the 15th international conference on World Wide Web
  • Year:
  • 2006

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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).