Towards scaling fully personalized PageRank (2004)
| Venue: | In Proceedings of the 3rd Workshop on Algorithms and Models for the Web-Graph (WAW |
| Citations: | 45 - 2 self |
BibTeX
@INPROCEEDINGS{Fogaras04towardsscaling,
author = {Dániel Fogaras and Balázs Rácz},
title = {Towards scaling fully personalized PageRank},
booktitle = {In Proceedings of the 3rd Workshop on Algorithms and Models for the Web-Graph (WAW},
year = {2004},
pages = {105--117}
}
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Abstract
Abstract 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. 1
