Future Work

Chapter

Abstract

In this chapter, we discuss the possible future work on learning to rank. In particular, we show some potential research topics along the following directions: sample selection bias, direct learning from logs, feature engineering, advanced ranking models, large-scale learning to rank, online complexity, robust learning to rank, and online learning to rank. At the end of this chapter, we will make brief discussions on the new scenarios beyond ranking, which seems to be the future trend of search. Algorithmic and theoretical discussions on the new scenario may lead to another promising research direction.

This is a preview of subscription content, log in to check access.

References

  1. 1.
    Chang, Y., Liu, T.Y.: Future directions in learning to rank. JMLR: Workshop and Conference Proceedings 14, 91–100 (2011)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bickel, S., Scheffer, T.: Dirichlet-enhanced spam filtering based on biased samples. In: Advances in Neural Information Processing Systems 19 (NIPS 2006) (2007)Google Scholar
  3. 3.
    Cambazoglu, B., Zaragoza, H., Chapelle, O., Chen, J., Liao, C., Zheng, Z., Degenhardt, J.: Early exit optimizations for additive machine learned ranking systems. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM 2010), pp. 411–420 (2010)CrossRefGoogle Scholar
  4. 4.
    Chang, E., Zhu, K., Wang, H., Bai, H., Li, J., Qiu, Z., Cui, H.: Parallelizing support vector machines on distributed computers. In: Platt, J., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems 20 (NIPS 2007), pp. 257–264. MIT Press, Cambridge (2008)Google Scholar
  5. 5.
    Chu, C.T., Kim, S.K., Lin, Y.A., Yu, Y., Bradski, G., Ng, A., Olukotun, K.: Map-reduce for machine learning on multicore. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19 (NIPS 2006), pp. 281–288. MIT Press, Cambridge (2007)Google Scholar
  6. 6.
    Dong, A., Chang, Y., Zheng, Z., Mishne, G., Bai, J., Zhang, R., Buchner, K., Liao, C., Diaz, F.: Towards recency ranking in web search. In: Proceedings of 3rd ACM International Conference on Web Search and Data Mining (WSDM 2010) (2010)Google Scholar
  7. 7.
    Guiver, J., Snelson, E.: Bayesian inference for Plackett–Luce ranking models. In: Proceedings of the 26th International Conference on Machine Learning (ICML 2009), pp. 377–384 (2009)Google Scholar
  8. 8.
    Inagaki, Y., Sadagopan, N., Dupret, G., Liao, C., Dong, A., Chang, Y., Zheng, Z.: Session based click features for recency ranking. In: Proceedings of 24th AAAI Conference on Artificial Intelligence (AAAI 2010) (2010)Google Scholar
  9. 9.
    Klementiev, A., Roth, D., Small, K.: Unsupervised rank aggregation with distance-based models. In: Proceedings of the 25th International Conference on Machine Learning (ICML 2008), pp. 472–479 (2008)CrossRefGoogle Scholar
  10. 10.
    Li, X., Li, F., Ji, S., Zheng, Z., Dong, A., Chang, Y.: Incorporating robustness into web ranking evaluation. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM 2009) (2009)Google Scholar
  11. 11.
    Qin, T., Geng, X., Liu, T.Y.: A new probabilistic model for rank aggregation. In: Lafferty, J., Williams, C.K.I., Shawe-Taylor, J., Zemel, R., Culotta, A. (eds.) Advances in Neural Information Processing Systems 23 (NIPS 2010), pp. 1948–1956 (2011)Google Scholar
  12. 12.
    Qin, T., Liu, T.Y., Zhang, X.D., Wang, D., Li, H.: Learning to rank relational objects and its application to web search. In: Proceedings of the 17th International Conference on World Wide Web (WWW 2008), pp. 407–416 (2008)CrossRefGoogle Scholar
  13. 13.
    Qin, T., Liu, T.Y., Zhang, X.D., Wang, D.S., Li, H.: Global ranking using continuous conditional random fields. In: Advances in Neural Information Processing Systems 21 (NIPS 2008), pp. 1281–1288 (2009)Google Scholar
  14. 14.
    Radlinski, F., Kleinberg, R., Joachims, T.: Learning diverse rankings with multi-armed bandits. In: Proceedings of the 25th International Conference on Machine Learning (ICML 2008), pp. 784–791 (2008)CrossRefGoogle Scholar
  15. 15.
    Sipser, M.: Introduction to the Theory of Computation, 2nd edn. PWS Publishing, Boston (2006)MATHGoogle Scholar
  16. 16.
    Ueda, N., Nakano, R.: Generalization error of ensemble estimators. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN 1996), pp. 90–95 (1996)CrossRefGoogle Scholar
  17. 17.
    Viola, P.A., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  18. 18.
    Wang, L., Lin, J., Metzler, D.: Learning to efficiently rank. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2010), pp. 138–145 (2010)CrossRefGoogle Scholar
  19. 19.
    Zadrozny, B.: Learning and evaluating classifiers under sample selection bias. In: Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004) (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Microsoft Research AsiaBeijingPeople’s Republic of China

Personalised recommendations