Information Technology Reference
In-Depth Information
4.9 In practice, people care more about the correct ranking at the top positions of
the ranking result. Accordingly, the true loss should not be a permutation-level
0-1 loss, but should be defined with respect to the top-
k
subgroup. Prove that
in this new situation, ListMLE cannot lead to the optimal ranker in terms of
the true loss. Show how to modify the loss function of ListMLE such that its
minimization can minimize the top-
k
true loss.
References
1. Almeida, H., Goncalves, M., Cristo, M., Calado, P.: A combined component approach for
finding collection-adapted ranking functions based on genetic programming. In: Proceedings
of the 30th Annual International ACM SIGIR Conference on Research and Development in
Information Retrieval (SIGIR 2007), pp. 399-406 (2007)
2. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance
learning. In: Advances in Neural Information Processing Systems 15 (NIPS 2002), pp. 561-
568 (2003)
3. Burges, C.J., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.:
Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference
on Machine Learning (ICML 2005), pp. 89-96 (2005)
4. Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to
listwise approach. In: Proceedings of the 24th International Conference on Machine Learning
(ICML 2007), pp. 129-136 (2007)
5. Chakrabarti, S., Khanna, R., Sawant, U., Bhattacharyya, C.: Structured learning for non-
smooth ranking losses. In: Proceedings of the 14th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining (KDD 2008), pp. 88-96 (2008)
6. Chapelle, O., Le, Q., Smola, A.: Large margin optimization of ranking measures. In: NIPS
2007 Workshop on Machine Learning for Web Search (2007)
7. Chapelle, O., Wu, M.: Gradient descent optimization of smoothed information retrieval met-
rics. Information Retrieval Journal. Special Issue on Learning to Rank 13(3), doi:
10.1007/
s10791-009-9110-3
(2010)
8. Fan, W., Fox, E.A., Pathak, P., Wu, H.: The effects of fitness functions on genetic programming
based ranking discovery for web search. Journal of American Society for Information Science
and Technology
55
(7), 628-636 (2004)
9. Fan, W., Gordon, M., Pathak, P.: Discovery of context-specific ranking functions for effective
information retrieval using genetic programming. IEEE Transactions on Knowledge and Data
Engineering
16
(4), 523-527 (2004)
10. Fan, W., Gordon, M., Pathak, P.: A generic ranking function discovery framework by genetic
programming for information retrieval. Information Processing and Management
40
(4), 587-
602 (2004)
11. Fan, W., Gordon, M., Pathak, P.: Genetic programming-based discovery of ranking functions
for effective web search. Journal of Management of Information Systems
21
(4), 37-56 (2005)
12. Fan, W., Gordon, M., Pathak, P.: On linear mixture of expert approaches to information re-
trieval. Decision Support System
42
(2), 975-987 (2006)
13. Fan, W., Gordon, M.D., Xi, W., Fox, E.A.: Ranking function optimization for effective web
search by genetic programming: an empirical study. In: Proceedings of the 37th Hawaii Inter-
national Conference on System Sciences (HICSS 2004), p. 40105 (2004)
14. Guiver, J., Snelson, E.: Learning to rank with softrank and Gaussian processes. In: Proceedings
of the 31st Annual International ACM SIGIR Conference on Research and Development in
Information Retrieval (SIGIR 2008), pp. 259-266 (2008)
15. 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)
