Top-d Rank Aggregation in Web Meta-search Engine

(Extended Abstract)
  • Qizhi Fang
  • Han Xiao
  • Shanfeng Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6213)


In this paper, we consider the rank aggregation problem for information retrieval over Web making use of a kind of metric, the coherence, which considers both the normalized Kendall-τ distance and the size of overlap between two partial rankings. In general, the top-d coherence aggregation problem is defined as: given collection of partial rankings Π = {τ 1,τ 2, ⋯ , τ K }, how to find a final ranking π with specific length d, which maximizes the total coherence \(\Phi(\pi,\Pi)=\sum_{i=1}^K \Phi(\pi,\tau_i)\). The corresponding complexity and algorithmic issues are discussed in this paper. Our main technical contribution is a polynomial time approximation scheme (PTAS) for a restricted top-d coherence aggregation problem.


Rank aggregation Kendall-τ distance coherence \(\cal {NP}\)-hard approximate algorithm PTAS 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arora, S., Frieze, A., Kaplan, H.: A new rounding procedure for the assignment problem with applications to dense graph arrangement problems. Mathematical Programming, Ser. A 92, 1–36 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Arora, S., Karger, D., Karpinski, M.: Polynomial-time approximation schemes for dense instances of NP-hard optimization problems. Journal of Computer and System Sciences 58, 193–210 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Barthelemy, J.P., Guenoche, A., Hudry, O.: Median linear orders: Heuristics and a branch and bound algorithm. European Journal of Operational Research 42, 313–325 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Chin, F.Y.L., Deng, X., Fang, Q., Zhu, S.: Approximate and dynamic rank aggregation. Theoretical Computer Science 352, 409–424 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. WWW10, pp. 613–622 (2001)Google Scholar
  6. 6.
    Gravano, L., Chang, C., Garcia-Molina, H., Paepcke, A.: STARTS: Stanford proposal for internet meta-searching. ACM SIGMOD, Tucson, 207–218 (May 1997)Google Scholar
  7. 7.
    Hoelscher, C.: How Internet Experts Search for Information on the Web. In: The World Conference of the World Wide Web, Internet, and Intranet, Orlando, FL (1998)Google Scholar
  8. 8.
    Jansen, B.J., Spink, A., Saracevic, T.: Real life, real users, and real needs: a study and analysis of user queries on the web. Information Processing and Management 36, 207–227 (2000)CrossRefGoogle Scholar
  9. 9.
    Raghavan, P., Thompson, C.: Randomized rounding: a technique for provably good algorithms and algorithmic proofs. Combinatorica 7, 365–374 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Silverstein, C., Henzinger, M., Marais, H., Moricz, M.: Analysis of a very large altavista query log. Technical Report SRC 1998-014, Digital Systems Research Center (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Qizhi Fang
    • 1
  • Han Xiao
    • 1
  • Shanfeng Zhu
    • 2
  1. 1.Department of MathematicsOcean University of ChinaQingdaoP.R. China
  2. 2.School of Computer Science and Shanghai Key Lab of Intelligent Information ProcessingFudan UniversityShanghaiP.R. China

Personalised recommendations