skip to main content
10.1145/1062745.1062824acmconferencesArticle/Chapter ViewAbstractPublication PageswwwConference Proceedingsconference-collections
Article

On the feasibility of low-rank approximation for personalized PageRank

Authors Info & Claims
Published:10 May 2005Publication History

ABSTRACT

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 low-rank approximation of the Page-Rank transition matrix; by our algorithm we may compute an approximate personalized Page-Rank by multiplying an n x k, a k x 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.

References

  1. P. Drineas, M. W. Mahoney, and R. Kannan. Fast monte carlo algorithms for matrices II. Technical Report YALEU/DCS/TR-1270, Yale University, 2004.Google ScholarGoogle Scholar
  2. D. Fogaras and B. Racz. Towards scaling fully personalized pagerank. In WAW, pages 105--117, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  3. H. Guo. IRR: An algorithm for computing the smallest singular value of large-scale matrices. International J. of Computer Math., 77(1):89--104, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  4. T. H. Haveliwala. Topic-sensitive PageRank. In Proceedings of the 11th World Wide Web Conference (WWW), Honolulu, Hawaii, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. G. Jeh and J. Widom. Scaling personalized web search. In Proceedings of the 12th World Wide Web Conference (WWW), pages 271--279. ACM Press, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project, 1998.Google ScholarGoogle Scholar
  7. P. K. C. Singitham, M. S. Mahabhashyam, and P. Raghavan. Efficiency-quality tradeoffs for vector score aggregation. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB), 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. On the feasibility of low-rank approximation for personalized PageRank

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!