Comparing partial rankings

SIAM Journal on Discrete Mathematics

Volumn 20, Issue 3, 2006, Pages 628-648

  Fagin, Ronald ^a   Kumar, Ravi ^b   Mahdian, Mohammad ^c   Sivakumar, D ^d   Vee, Erik ^a  

^a IBM ALMADEN RESEARCH CENTER   (United States)

^b YAHOO RESEARCH   (United States)

^c MICROSOFT RESEARCH   (United States)

^d GOOGLE INC   (United States)

Author keywords

Bucket order; Metric; Partial ranking; Permutation

Indexed keywords

METRIC SYSTEM; PARTIAL DIFFERENTIAL EQUATIONS;

BUCKET ORDER; PARTIAL RANKING; PERMUTATION;

FINITE DIFFERENCE METHOD;

EID: 34547922052     PISSN: 08954801     EISSN: None     Source Type: Journal    
DOI: 10.1137/05063088X     Document Type: Article

References (29)

1. F. ALESKEROV AND B. MONJARDET, Utility Maximization, Choice, and Preference, Stud. Econom. Theory 16, Springer-Verlag, Berlin, 2002.
2. K. J. ARROW, Social Choice and Individual Values, John Wiley and Sons, New York, 1951.
3. K. J. ARROW AND M. D. INTRILIGATOR, Handbook of Mathematical Economics, North-Holland, Amsterdam, 1982.
4. J. A. ASLAM AND M. MONTAGUE, Models for metasearch, in Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New Orleans, 2001, pp. 276-284.
5. K. A. BAGGERLY, Visual Estimation of Structure in Ranked Data, Ph.D. thesis, Rice University, Houston, TX, 1995.
6. J. J. BARTHOLDI, C. A. TOVEY, AND M. A. TRICK, Voting schemes for which it can be difficult to tell who won the election, Social Choice and Welfare, 6 (1989), pp. 157-165.
7. D. S. BRIDGES AND G. B. MEHTA, Representations of Preference Orderings, Lecture Notes in Econom. and Math. Systems 422, Springer-Verlag, Heidelberg, Berlin, New York, 1995.
8. W. W. COHEN, R. E. SCHAPIRE, AND Y. SINGER, Learning to order things, J. Artificial Intelligence Res., 10 (1999), pp. 243-270.
9. D. E. CRITCHLOW, Metric Methods for Analyzing Partially Ranked Data, Lecture Notes in Statist. 34, Springer-Verlag, Berlin, 1980.
10. P. DIACONIS, Group Representation in Probability and Statistics, IMS Lecture Series Monogr. Ser. 11, Institute of Mathematical Statistics, Hayward, CA, 1988.
11. P. DIACONIS AND R. GRAHAM, Spearman's footrule as a measure of disarray, J. Roy. Statist. Soc. Ser. B, 39 (1977), pp. 262-268.
12. C. DWORK, R. KUMAR, M. NAOR, AND D. SIVAKUMAR, Rank aggregation methods for the web, in Proceedings of the 10th International World Wide Web Conference, Hong Kong, 2001, pp. 613-622.
13. J. EATWELL, M. MILGATE, AND P. NEWMAN, The New Palgrave: A Dictionary of Economics, MacMillan, London, 1987.
14. R. FAGIN, R. KUMAR, M. MAHDIAN, D. SIVAKUMAR, AND E. VEE, Comparing and aggregating rankings with ties, in Proceedings of the 23rd ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Denver, 2004, pp. 47-58.
15. R. FAGIN, R. KUMAR, K. MCCURLEY, J. NOVAK, D. SIVAKUMAR, J. TOMLIN, AND D. WILLIAMSON, Searching the workplace web, in Proceedings of the 12th International World Wide Web Conference, Budapest, 2003, pp. 366-375.
16. R. FAGIN, R. KUMAR, AND D. SIVAKUMAR, Comparing top k lists, SIAM J. Discrete Math., 17 (2003), pp. 134-160.
17. R. FAGIN, R. KUMAR, AND D. SIVAKUMAR, Efficient similarity search and classification via rank aggregation, in Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, San Diego, 2003, pp. 301-312.
18. P. FISHBURN, Condorcet social choice functions, SIAM J. Appl. Math., 33 (1977), pp. 469-489.
19. P. C. FISHBURN, Interval Orders and Interval Graphs: A Study of Partially Ordered Sets, John Wiley and Sons, New York, 1985.
20. L. A. GOODMAN AND W. H. KRUSKAL, Measures of association for cross classification, J. Amer. Statist. Assoc., 49 (1954), pp. 732-764.
21. T. H. HAVELIWALA, A. GIONIS, D. KLEIN, AND P. INDYK, Evaluating strategies for similarity search on the web, in Proceedings of the 11th International World Wide Web Conference, Honolulu, 2002, pp. 432-442.
22. M. KENDALL AND J. D. GIBBONS, Rank Correlation Methods, Edward Arnold, London, 1990.
23. M. G. KENDALL, The treatment of ties in ranking problems, Biometrika, 33 (1945), pp. 239-251.
24. G. LEBANON AND J. D. LAFFERTY, Cranking: Combining rankings using conditional probability models on permutations, in Proceedings of the 19th International Conference on Machine Learning, Sydney, Australia, 2002, pp. 363-370.
25. M. MONTAGUE AND J. A. ASLAM, Condorcet fusion for improved retrieval, in Proceedings of the 11th International Conference on Information and Knowledge Management, McLean, VA, 2002, pp. 538-548.
26. M. E. RENDA AND U. STRACCIA, Web metasearch: Rank vs. score based rank aggregation methods, in Proceedings of the 18th Annual Symposium on Applied Computing, Melbourne, FL, 2003, pp. 841-846.
27. F. S. ROBERTS, Measurement Theory, with Applications to Decisionmaking, Utility, and the Social Sciences, Encyclopedia Math. Appl., Addison-Wesley, Reading, MA, 1979.
28. J. SESE AND S. MORISHITA, Rank aggregation method for biological databases, Genome Informatics, 12 (2001), pp. 506-507.
29. R. R. YAGER AND V. KREINOVICH, On how to merge sorted lists coming from different web search tools, Soft Computing Research Journal, 3 (1999), pp. 83-88.