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...

• Type: Conference
• Year: 2005
• Where: WWW
• Authors: András A. Benczúr, Károly Csalogány, Tamás Sarlós
• Keywords: Approximate Personalized Pagerank, Internet Technology, Keywords Personalized PageRank, Personalized Pagerank, WWW 2005