On the feasibility of low-rank approximation for personalized PageRank

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On the feasibility of low-rank approximation for personalized PageRank
Personalized PageRank expresses backlink-based page quality around user-selected pages in a similar way to PageRank over the entire Web. Algorithms for computing personalized PageRank on the fly are either limited to a restricted choice of page selection or believed to behave well only on sparser regions of the Web. In this paper we show the feasibility of computing personalized PageRank by a k < 1000 lowrank approximation of the PageRank transition matrix; by our algorithm we may compute an approximate personalized PageRank by multiplying an n ? k, a k ? n matrix and the n-dimensional personalization vector. Since low-rank approximations are accurate on dense regions, we hope that our technique will combine well with known algorithms. Categories and Subject Descriptors F.2.0 [Analysis of Algorithms and Problem Complexity]: General; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms Algorithms, Performance, Experimentation, Measurement Keyword...
András A. Benczúr, Károly Csa
Added 22 Nov 2009
Updated 22 Nov 2009
Type Conference
Year 2005
Where WWW
Authors András A. Benczúr, Károly Csalogány, Tamás Sarlós
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