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

• Type: Conference
• Year: 2006
• Where: WWW
• Authors: Matthew Richardson, Amit Prakash, Eric Brill
• Keywords: Internet Technology, Keywords Static Ranking, Static Features, Static Ranking Pairwise, WWW 2006