Abstract
The RBS algorithm is a novel link-based algorithm for ranking results of a search engine. RBS may be viewed as an extension of PageRank by a parameterized “back button” modeling. RBS is based on the “random surfer with back step” model [7] similarly as PageRank is based on the simpler “random surfer” model [4]. To scale to real Web RBS computes merely a fast probabilistic approximation of the ranking induced by the “random surfer with back step” model [6].
In this paper we experimentally measure the quality of this approximation using a high quality synthetic Web evolution model [5] of our own implementation.
The results demonstrate that RBS is a very good approximation to the “ideal” ranking. Furthermore, as the experiment shows, RBS clearly outperforms PageRank in “back step” modeling even if we try to parameterize the latter.
This paper is based on fragments of the author’s PhD thesis [6]
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References
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Sydow, M. (2005). Approximation Quality of the RBS Ranking Algorithm. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_30
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DOI: https://doi.org/10.1007/3-540-32392-9_30
Publisher Name: Springer, Berlin, Heidelberg
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