An important problem in Web search is to determine the importance of each page. This problem consists in computing, by the power method, the left principal eigenvector (the PageRank vector) of a matrix depending on a parameter $c$ which has to be chosen close to 1. However, when $c$ is close to 1, the problem is ill-conditioned, and the power method converges slowly. So, the idea developed in this paper consists in computing the PageRank vector for several values of $c$, and then to extrapolate them, by a conveniently chosen rational function, at a point near 1. The choice of this extrapolating function is based on the mathematical considerations about the PageRank vector.
@InProceedings{brezinski_et_al:DagSemProc.07071.9, author = {Brezinski, Claude and Redivo-Zaglia, Michela}, title = {{Extrapolation and minimization procedures for the PageRank vector}}, booktitle = {Web Information Retrieval and Linear Algebra Algorithms}, pages = {1--6}, series = {Dagstuhl Seminar Proceedings (DagSemProc)}, ISSN = {1862-4405}, year = {2007}, volume = {7071}, editor = {Andreas Frommer and Michael W. Mahoney and Daniel B. Szyld}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07071.9}, URN = {urn:nbn:de:0030-drops-10682}, doi = {10.4230/DagSemProc.07071.9}, annote = {Keywords: Extrapolation, PageRank, Web matrix, eigenvector computation.} }
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