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Learning and Optimizing with Preferences

  • Conference paper
Algorithmic Learning Theory (ALT 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8139))

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Abstract

Preferences and choices are a central source of information generated by humans. They have been studied for centuries in the context of social choice theory, econometric theory, statistics and psychology. At least two Nobel prizes in economics have been awarded for work reasoning about human preferences and choices.

In the last two decades computer scientists have studied preference data, which became available in unprecedented quantities: Each time we click or tap on a search result, a sponsored ad or a product recommendation, we express preference of one alternative from a small set of alternatives. Additionally, many crowsdsourcing systems explicitly ask (paid?) experts to solicit preferences or even full rankings of alternative sets.

What are the advantages of preferences compared to other forms of information, and what combinatorial and learning theoretical challenges do they give rise to? I will present important problems and survey results.

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References

  1. Ailon, N., Begleiter, R., Ezra, E.: Active learning using smooth relative regret approximations with applications. Journal of Machine Learning Research - Proceedings Track 23 (2012)

    Google Scholar 

  2. Ailon, N., Charikar, M., Newman, A.: Aggregating inconsistent information: Ranking and clustering. J. ACM 55(5) (2008)

    Google Scholar 

  3. Arrow, K.: Social Choice and Individual Values. Yale University Press (1963)

    Google Scholar 

  4. Balcan, M.-F., Beygelzimer, A., Langford, J.: Agnostic active learning. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 65–72 (2006)

    Google Scholar 

  5. Bartholdi, J., Tovey, C.A., Trick, M.A.: Voting schemes for which it can be difficult to tell who won the election. Social Choice and Welfare 6(2), 157–165 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  6. Borda, J.C.: Mémoire sur les élections au scrutin. Histoire de l’Académie Royale des Sciences (1781)

    Google Scholar 

  7. Braverman, M., Mossel, E.: Noisy sorting without resampling. In: SODA 2008: Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 268–276 (2008)

    Google Scholar 

  8. Carterette, B., Bennett, P.N., Chickering, D.M., Dumais, S.T.: Here or there: Preference judgments for relevance. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 16–27. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. J. Artif. Intell. Res. (JAIR) 10, 243–270 (1999)

    MathSciNet  MATH  Google Scholar 

  10. Condorcet, M.-J.: Éssai sur l’application de l’analyse à la probabilité des décisions rendues à la pluralité des voix. Imprimerie Royal, Paris (1785)

    Google Scholar 

  11. Coppersmith, D., Fleischer, L.K., Rurda, A.: Ordering by weighted number of wins gives a good ranking for weighted tournaments. ACM Trans. Algorithms 6(3), 1–13 (2010)

    Article  MathSciNet  Google Scholar 

  12. Diaconis, P., Graham, R.: Spearman’s footrule as a measure of disarray. Journal of the Royal Statistical Society, Series B 39(2), 262–268 (1977)

    MathSciNet  MATH  Google Scholar 

  13. Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proceedings of the Tenth International Conference on the World Wide Web, WWW 2010, Hong Kong, pp. 613–622 (2001)

    Google Scholar 

  14. Fagin, R., Kumar, R., Mahdian, M., Sivakumar, D., Vee, E.: Comparing and aggregating rankings with ties. In: Proceedings of the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 47–58 (2004)

    Google Scholar 

  15. Fang, Y., Si, L.: A latent pairwise preference learning approach for recommendation from implicit feedback. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM (2012)

    Google Scholar 

  16. Farias, V.F., Jagabathula, S., Shah, D.: Sparse choice models. In: CISS, pp. 1–28 (2012)

    Google Scholar 

  17. Feige, U., Peleg, D., Raghavan, P., Upfal, E.: Computing with unreliable information. In: STOC 1990: Proceedings of the Twenty-Second Annual ACM Symposium on Theory of Computing, pp. 128–137 (1990)

    Google Scholar 

  18. De Gemmis, M., Iaquinta, L., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Preference learning in recommender systems. In: Preference Learning (PL 2009) ECML/PKDD 2009 Workshop (2009)

    Google Scholar 

  19. Hanneke, S.: A bound on the label complexity of agnostic active learning. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 353–360 (2007)

    Google Scholar 

  20. Hüllermeier, E., Fürnkranz, J., Cheng, W., Brinker, K.: Label ranking by learning pairwise preferences. Artif. Intell. 172(16-17), 1897–1916 (2008)

    Article  MATH  Google Scholar 

  21. Jiang, X., Lim, L.-H., Yao, Y., Ye, Y.: Statistical ranking and combinatorial hodge theory. Math. Program. 127(1), 203–244 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  22. Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142 (2002)

    Google Scholar 

  23. Kemeny, J.G.: Mathematics without numbers. Daedalus 88, 571–591 (1959)

    Google Scholar 

  24. Kenyon-Mathieu, C., Schudy, W.: How to rank with few errors. In: STOC, pp. 95–103 (2007)

    Google Scholar 

  25. Marden, J.I.: Analyzing and Modeling Rank Data. Chapman & Hall (1995)

    Google Scholar 

  26. Radinsky, K., Ailon, N.: Ranking from pairs and triplets: information quality, evaluation methods and query complexity. In: WSDM, pp. 105–114 (2011)

    Google Scholar 

  27. Radlinski, F., Kleinberg, R., Joachims, T.: Learning diverse rankings with multi-armed bandits. In: ICML, pp. 784–791 (2008)

    Google Scholar 

  28. Rudin, C.: The p-norm push: A simple convex ranking algorithm that concentrates at the top of the list. J. Mach. Learn. Res. 10, 2233–2271 (2009)

    MathSciNet  MATH  Google Scholar 

  29. Train, K.: Discrete Choice Methods with Simulation. Cambridge University Press (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Technion Israel Institute of Technology, Haifa, Israel

    Nir Ailon

Authors
  1. Nir Ailon
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Editor information

Editors and Affiliations

  1. National University of Singapore, Republic of Singapore

    Sanjay Jain  & Frank Stephan  & 

  2. Inria Lille - Nord Europe, Villeneuve d’Ascq, France

    Rémi Munos

  3. Hokkaido University, Sapporo, Japan

    Thomas Zeugmann

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© 2013 Springer-Verlag Berlin Heidelberg

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Ailon, N. (2013). Learning and Optimizing with Preferences. In: Jain, S., Munos, R., Stephan, F., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2013. Lecture Notes in Computer Science(), vol 8139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40935-6_2

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  • DOI: https://doi.org/10.1007/978-3-642-40935-6_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40934-9

  • Online ISBN: 978-3-642-40935-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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