[1]
J. Fürnkranz and E. Hüllermeier, Preference Learning. Springer-Verlag, 2010.
[2]
T. Joachims, Optimizing search engines using clickthrough data, in Proc. of SIGKDD, 2002, pp. 133-142.
[3]
O. Dekel, C. D. Manning, and Y. Singer, Log-linear models for label ranking, in Proc. of NIPS, 2004, pp. 209-216.
[4]
F. Aiolli and A. Sperduti, Learning preferences for multiclass problems, in Proc. of NIPS, 2005, pp. 17-24.
[5]
W. Chu and Z. Ghahramani, Preference learning with Gaussian processes, in Proc. of ICML, 2005, pp. 137-144.
[6]
S. Har-Peled, D. Roth, and D. Zimak, Constraint classification: A new approach to multiclass classification and ranking, in Proc. of NIPS, 2003, pp. 785-792.
[7]
P. Li, C. Burges, and Q. Wu, McRank: Learning to rank using multiple classification and gradient boosting, in NIPS, 2008, pp. 897-904.
[8]
Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer, An efficient boosting algorithm for combining preferences, Journal of Machine Learning Research, vol. 4, pp. 933-969, 2003.
[9]
W. Chu and S. S. Keerthi, New approaches to support vector ordinal regression, in Proc. of ICML, 2005, pp. 145-152.
[10]
W. Chu and Z. Ghahramani, Gaussian processes for ordinal regression, Journal of Machine Learning Research, vol. 6, pp. 1-48, 2005.
[11]
B. Eric, N. D. Freitas, and A. Ghosh, Active preference learning with discrete choice data, in Proc. of NIPS, 2008, pp. 409-416.
[12]
E. Bonilla, S. Guo, and S. Sanner, Gaussian process preference elicitation, in Proc. of NIPS, 2010, pp. 262-270.
[13]
C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. The MIT Press, 2006.
[14]
B. Schölkopf and A. Smola, Learning with Kernels. The MIT Press, 2002.
[15]
W. W. Cohen, R. E. Schapire, and Y. Singer, Learning to order things, in Proc. of NIPS, 1998, pp. 451-457.
[16]
K. Crammer and Y. Singer, Pranking with ranking, in Proc. of NIPS, 2002, pp. 641-648.
[17]
A. Shashua and A. Levin, Ranking with large margin principle: Two approaches, in Proc. of NIPS, 2003, pp. 937-944.
[18]
C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender, Learning to rank using gradient descent, in Proc. of ICML, 2005, pp. 89-96.
[19]
Y. Yue, T. Finley, F. Radlinski, and T. Joachims, A support vector method for optimizing average precision, in Proc. of SIGIR, 2007, pp. 271-278.
[20]
Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li, Learning to rank: From pairwise approach to listwise approach, in Proc. of ICML, 2007, pp. 129-136.
[21]
F. Xia, T.-Y. Liu, J. Wang, W. Zhang, and H. Li, Listwise approach to learning to rank: Theory and algorithm, in Proc. of ICML, 2008, pp. 1192-1199.
[22]
M. N. Volkovs and R. S. Zemel, Boltzrank: Learning to maximize expected ranking gain, in Proc. of ICML, 2009, pp. 1089-1096.
[23]
H. Valizadegan, R. Jin, R. Zhang, and J. Mao, Learning to rank by optimizing NDCG measure, in Proc. of NIPS, 2009, pp. 1883-1891.
[24]
T.-Y. Liu, Learning to rank for information retrieval, Foundations and Trends in Information Retrieval, vol. 3, no. 3, pp. 225-331, 2009.
[25]
E. Hüllermeier, J. Fürnkranz, W. Cheng, and K. Brinker, Label ranking by learning pairwise preferences, Artificial Intelligence, vo. 172, no. 16-17, pp. 1897-1916, 2008.
[26]
W. Cheng, J. Hühn, and E. Hüllermeier, Decision tree and instance-based learning for label ranking, in Proc. of ICML, 2009, pp. 161-168.
[27]
W. Chu, V. Sindhwani, Z. Ghahramani, and S. S. Keerthi, Relational learning with Gaussian processes, in Proc. of NIPS, 2007, pp. 289-296.
[28]
R. Silva, W. Chu, and Z. Ghahramani, Hidden common cause relations in relational learning, in Proc. of NIPS, 2008, pp. 1345-1352.
[29]
W.-J. Li, Z. Zhang, and D.-Y. Yeung, Latent Wishart processes for relational kernel learning, in Proc. of AISTATS, 2009, pp. 336-343.
[30]
Y. W. Teh, M. Seeger, and M. I. Jordan, Semiparametric latent factor models, in Proc. of AISTATS, 2005, pp. 333-340.
[31]
T. Qin, T.-Y. Liu, J. Xu, and H. Li, LETOR: A benchmark collection for research on learning to rank for information retrieval, Information Retrieval, vol. 13, no. 4, pp. 346-374, 2010.
[32]
W. Hersh, C. Buckley, T. J. Leone, and D. Hickam, OHSUMED: An interactive retrieval evaluation and new large test collection for research, in Proc. of SIGIR, 1994, pp. 192-201.
[33]
N. Craswell and D. Hawking, Overview of the TREC-2004 web track, in NIST Special Publication, 2004, pp. 500-261.
[34]
K. Kersting and Z. Xu, Learning preferences with hidden common cause relations, in Proc. of ECML/PKDD, 2009, pp. 676-691.
