-
2
-
-
36448971554
-
The relationship between IR effectiveness measures and user satisfaction
-
A. Al-Maskari, M. Sanderson, and P. Clough. The relationship between IR effectiveness measures and user satisfaction. In SIGIR, pages 773-774, 2007.
-
(2007)
SIGIR
, pp. 773-774
-
-
Al-Maskari, A.1
Sanderson, M.2
Clough, P.3
-
5
-
-
84859817220
-
Robust reductions from ranking to classification
-
M.F. Balcan, N. Bansal, A. Beygelzimer, D. Coppersmith, J. Langford, and G.B. Sorkin. Robust reductions from ranking to classification. Machine learning, 72(1): 139-153, 2008.
-
(2008)
Machine Learning
, vol.72
, Issue.1
, pp. 139-153
-
-
Balcan, M.F.1
Bansal, N.2
Beygelzimer, A.3
Coppersmith, D.4
Langford, J.5
Sorkin, G.B.6
-
6
-
-
33645505792
-
Convexity, classification, and risk bounds
-
P.L. Bartlett, M.I. Jordan, and J.D. McAuliffe. Convexity, classification, and risk bounds. Journal of the American Statistical Association, 101(473): 138-156, 2006.
-
(2006)
Journal of the American Statistical Association
, vol.101
, Issue.473
, pp. 138-156
-
-
Bartlett, P.L.1
Jordan, M.I.2
McAuliffe, J.D.3
-
8
-
-
84864039510
-
Learning to rank with nonsmooth cost functions
-
The MIT Press
-
C.J.C. Burges, R. Ragno, and Q. V. Le. Learning to rank with nonsmooth cost functions. In Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, Volume 19, page 193. The MIT Press, 2007.
-
(2007)
Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference
, vol.19
, pp. 193
-
-
Burges, C.J.C.1
Ragno, R.2
Le, Q.V.3
-
9
-
-
84877756508
-
On the (non-)existence of convex, calibrated surrogate losses for ranking
-
P. Bartlett, F.C.N. Pereira, C.J.C. Burges, L. Bottou, and K.Q. Weinberger, editors
-
Clément Calauzènes, Nicolas Usunier, and Patrick Gallinari. On the (non-)existence of convex, calibrated surrogate losses for ranking. In P. Bartlett, F.C.N. Pereira, C.J.C. Burges, L. Bottou, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 197-205. 2012.
-
(2012)
Advances in Neural Information Processing Systems 25
, pp. 197-205
-
-
Calauzènes, C.1
Usunier, N.2
Gallinari, P.3
-
10
-
-
34547987951
-
Learning to rank: From pairwise approach to listwise approach
-
Z. Cao, T. Qin, T.Y. Liu, M.F. Tsai, and H. Li. Learning to rank: from pairwise approach to listwise approach. In ICML, pages 129-136, 2007.
-
(2007)
ICML
, pp. 129-136
-
-
Cao, Z.1
Qin, T.2
Liu, T.Y.3
Tsai, M.F.4
Li, H.5
-
11
-
-
74549208546
-
Expected reciprocal rank for graded relevance
-
ACM
-
O. Chapelle, D. Metlzer, Y. Zhang, and P. Grinspan. Expected reciprocal rank for graded relevance. In Proceedings of the 18th ACM conference on Information and knowledge management, pages 621-630. ACM, 2009.
-
(2009)
Proceedings of the 18th ACM Conference on Information and Knowledge Management
, pp. 621-630
-
-
Chapelle, O.1
Metlzer, D.2
Zhang, Y.3
Grinspan, P.4
-
12
-
-
84858775754
-
Empirical performance maximization for linear rank statistics
-
D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors
-
Stéphan J.M. Clémençon and Nicolas Vayatis. Empirical performance maximization for linear rank statistics. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, pages 305-312. 2009.
-
(2009)
Advances in Neural Information Processing Systems 21
, pp. 305-312
-
-
Clémençon, S.J.M.1
Vayatis, N.2
-
13
-
-
51049098491
-
Ranking and empirical minimization of U-statistics
-
S. Clemençon, G. Lugosi, and N. Vayatis. Ranking and empirical minimization of U-statistics. The Annals of Statistics, 36(2): 844-874, 2008.
-
(2008)
The Annals of Statistics
, vol.36
, Issue.2
, pp. 844-874
-
-
Clemençon, S.1
Lugosi, G.2
Vayatis, N.3
-
14
-
-
55349114379
-
Statistical analysis of bayes optimal subset ranking
-
IEEE Transactions on
-
D. Cossock and T. Zhang. Statistical analysis of bayes optimal subset ranking. Information Theory, IEEE Transactions on, 54(11): 5140-5154, 2008.
-
(2008)
Information Theory
, vol.54
, Issue.11
, pp. 5140-5154
-
-
Cossock, D.1
Zhang, T.2
-
18
-
-
4644367942
-
An efficient boosting algorithm for combining preferences
-
ISSN 1532-4435
-
Y. Freund, R. Iyer, R.E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. The Journal of Machine Learning Research, 4: 933-969, 2003. ISSN 1532-4435.
-
(2003)
The Journal of Machine Learning Research
, vol.4
, pp. 933-969
-
-
Freund, Y.1
Iyer, R.2
Schapire, R.E.3
Singer, Y.4
-
25
-
-
0002282074
-
A new measure of rank correlation
-
M.G. Kendall. A new measure of rank correlation. Biometrika, 30(1/2): 81-93, 1938.
-
(1938)
Biometrika
, vol.30
, Issue.1-2
, pp. 81-93
-
-
Kendall, M.G.1
-
28
-
-
70450239631
-
The p-norm push: A simple convex ranking algorithm that concentrates at the top of the list
-
C. Rudin. The p-norm push: A simple convex ranking algorithm that concentrates at the top of the list. The Journal of Machine Learning Research, 10: 2233-2271, 2009.
-
(2009)
The Journal of Machine Learning Research
, vol.10
, pp. 2233-2271
-
-
Rudin, C.1
-
30
-
-
0004197424
-
-
Interscience Publishers Inc., New York
-
G. Sansone. Orthogonal Functions. Interscience Publishers Inc., New York, 1959.
-
(1959)
Orthogonal Functions
-
-
Sansone, G.1
-
33
-
-
80052413486
-
Learning to rank by optimizing NDCG measure
-
H. Valizadegan, R. Jin, R. Zhang, and J. Mao. Learning to rank by optimizing NDCG measure. Advances in Neural Information Processing Systems, 22: 1883-1891, 2009.
-
(2009)
Advances in Neural Information Processing Systems
, vol.22
, pp. 1883-1891
-
-
Valizadegan, H.1
Jin, R.2
Zhang, R.3
Mao, J.4
-
35
-
-
56449094442
-
Listwise approach to learning to rank: Theory and algorithm
-
ACM
-
F. Xia, T.Y. Liu, J. Wang, W. Zhang, and H. Li. Listwise approach to learning to rank: theory and algorithm. In Proceedings of the 25th international conference on Machine learning, pages 1192-1199. ACM, 2008.
-
(2008)
Proceedings of the 25th International Conference on Machine Learning
, pp. 1192-1199
-
-
Xia, F.1
Liu, T.Y.2
Wang, J.3
Zhang, W.4
Li, H.5
-
36
-
-
36448983903
-
A support vector method for optimizing average precision
-
ACM
-
Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 271-278. ACM, 2007.
-
(2007)
Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
, pp. 271-278
-
-
Yue, Y.1
Finley, T.2
Radlinski, F.3
Joachims, T.4
-
37
-
-
4644257995
-
Statistical behavior and consistency of classification methods based on convex risk minimization
-
T. Zhang. Statistical behavior and consistency of classification methods based on convex risk minimization. Annals of Statistics, pages 56-85, 2004a.
-
(2004)
Annals of Statistics
, pp. 56-85
-
-
Zhang, T.1
-
38
-
-
26944483874
-
Statistical analysis of some multi-category large margin classification methods
-
T. Zhang. Statistical analysis of some multi-category large margin classification methods. The Journal of Machine Learning Research, 5: 1225-1251, 2004b.
-
(2004)
The Journal of Machine Learning Research
, vol.5
, pp. 1225-1251
-
-
Zhang, T.1
|