Personalized PageRank expresses backlink-based page quality around user-selected pages in a similar way as PageRank expresses quality over the entire Web. Existing personalized PageRank algorithms can however serve on-line queries only for a restricted choice of page selection. In this paper we achieve full personalization by a novel algorithm that computes a compact database of simulated random walks; this database can serve arbitrary personal choices of small subsets of web pages. We prove that for a fixed error probability, the size of our database is linear in the number of web pages. We justify our estimation approach by asymptotic worst-case lower bounds; we show that exact personalized PageRank values can only be obtained from a database of quadratic size. © Springer-Verlag 2004.
CITATION STYLE
Fogaras, D., & Rácz, B. (2004). Towards scaling fully personalized pagerank. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3243, 105–117. https://doi.org/10.1007/978-3-540-30216-2_9
Mendeley helps you to discover research relevant for your work.