Enhancement of Fuzzy Rank Aggregation Technique

  • Mohd Zeeshan Ansari
  • M. M. Sufyan Beg
  • Manoj Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)

Abstract

The rankings of an object based on different criteria pose the problem of choice to give a ranking to that object at a position nearest to all the rankings. Generating a ranking list of such objects previously ranked is called rank aggregation. The aggregated ranking is analyzed by computing Spearman Footrule distance. The ranking list chosen by minimizing Spearman Footrule distance is NP-Hard problem even if number of lists is greater than four for partial lists. In the context of web, rank aggregation has been applied in meta-searching. However, the usage of prevailing search engines and meta-search engines, even though some of them being designated as successful, reveal that none of them have been effective in production of reliable and quality results, the reason being many. In order to improve the rank aggregation, we proposed the enhancement in the existing Modified Shimura technique by the introduction of a new OWA operator. It not only achieved better performance but also outperformed other similar techniques.

Keywords

Web Meta-searching Rank aggregation Spearman footrule distance Fuzzy logic 

References

  1. 1.
    Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the Tenth ACM International Conference on World Wide Web, pp. 613–622 (2001)Google Scholar
  2. 2.
    Akritidis, L., Katsaros, D., Bozanis, P.: Effective rank aggregation for meta searching. J. Syst. Softw. 84, 130–143 (2010)CrossRefGoogle Scholar
  3. 3.
    Renda, M.E., Straccia, U.: Web meta search: rank vs. score based rank aggregation methods. In: Proceedings of the ACM Symposium on Applied Computing, March 09–12 (2003)Google Scholar
  4. 4.
    Beg, M.M.S., Ahmad, N.: Soft computing techniques for rank aggregation on the world wide web. World Wide Web J.: Internet Inf. Syst. 6, 5–22 (2003)CrossRefGoogle Scholar
  5. 5.
    Aslam, J.A., Montague, M.: Models of meta search. In: Proceedings of 24th SIGIR 2001, pp. 276–284Google Scholar
  6. 6.
    Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation revisited. Manuscript (2001)Google Scholar
  7. 7.
    Beg, M.M.S., Ahmad, N.: Fuzzy logic and rank aggregation for the world wide web. Stud. Fuzziness Soft Comput. J. 137, 24–46 (2004)Google Scholar
  8. 8.
    Yasutake, S., Hatano, K., Takimoto, E., Takeda, M.: Online rank aggregation. In: Proceedings of 24th International Conference ALT 2013, pp. 68–82 (2013)Google Scholar
  9. 9.
    Qin, T., Geng, X., Liu, T.Y.: A new probabilistic model for rank aggregation. Proc. Adv. Neural Inf. Proc. Syst. 23, 681–689 (2010)Google Scholar
  10. 10.
    Liu, Y.T., Liu, T.Y., Qin, T., Ma, Z. M., Li, H.: Supervised rank aggregation. In: Proceedings of the ACM International Conference on World Wide Web, pp. 481–489 (2007)Google Scholar
  11. 11.
    Ailon, N.: Aggregation of partial rankings, p-ratings and top-m lists. Algorithmica 57(2), 284–300 (2008)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ross, T.J.: Fuzzy Logic with Engineering Applications. McGraw-Hill, New York (1997)Google Scholar
  13. 13.
    Shimura, M.: Fuzzy sets concepts in rank ordering objects. J. Math. Anal. Appl. 43, 717–733 (1973)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Borda, J.C.: Memoire sur les election au scrutiny. Histoire de l’Academie Royale des Sciences (1781)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Mohd Zeeshan Ansari
    • 1
  • M. M. Sufyan Beg
    • 2
  • Manoj Kumar
    • 3
  1. 1.Department of Computer EngineeringJamia Millia IslamiaNew DelhiIndia
  2. 2.Department of Computer EngineeringAligarh Muslim UniversityAligarhIndia
  3. 3.Department of Computer EngineeringDelhi Technological UniversityNew DelhiIndia

Personalised recommendations