Information Technology Reference
In-Depth Information
14.7 Exercises
14.1 Enumerate other potential applications of learning to rank in the field of com-
puter science.
14.2 Specify a field where learning-to-rank technologies can be potentially used,
and go through the entire process of applying learning-to-rank technologies
to advance its state of the art. Work items include a mathematical formulation
of the problem, feature extraction, loss function design, optimization method
implementation, etc.
14.3 Feature extraction is a key step in using learning-to-rank technologies in an
application, since it is usually the features that capture the unique characteris-
tics of the application. Use the examples given in the chapter to discuss how
the features extracted in each work reflect the characteristics of their target
1. Banerjee, S., Chakrabarti, S., Ramakrishnan, G.: Learning to rank for quantity consensus
queries. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Re-
search and Development in Information Retrieval (SIGIR 2009), pp. 243-250 (2009)
2. Brown, P., Pietra, S.D., Pietra, V.D., Mercer, R.: The mathematics of statistical machine trans-
lation: parameter estimation. Internet Mathematics 19 (2), 263-311 (1993)
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. Ciaramita, M., Murdock, V., Plachouras, V.: Online learning from click data for sponsored
search. In: Proceeding of the 17th International Conference on World Wide Web (WWW
2008), pp. 227-236 (2008)
6. Fang, Y., Si, L., Mathur, A.P.: Ranking experts with discriminative probabilistic models. In:
SIGIR 2009 Workshop on Learning to Rank for Information Retrieval (LR4IR 2009) (2009)
7. Herbrich, R., Obermayer, K., Graepel, T.: Large margin rank boundaries for ordinal regres-
sion. In: Advances in Large Margin Classifiers, pp. 115-132 (2000)
8. Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the 8th
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD
2002), pp. 133-142 (2002)
9. Mao, J.: Machine learning in online advertising. In: Proceedings of the 11th International
Conference on Enterprise Information Systems (ICEIS 2009), p. 21 (2009)
10. Metzler, D.A., Kanungo, T.: Machine learned sentence selection strategies for query-biased
summarization. In: SIGIR 2008 Workshop on Learning to Rank for Information Retrieval
(LR4IR 2008) (2008)
11. Pessiot, J.F., Truong, T.V., Usunier, N., Amini, M.R., Gallinari, P.: Learning to rank for collab-
orative filtering. In: Proceedings of the 9th International Conference on Enterprise Information
Systems (ICEIS 2007), pp. 145-151 (2007)
12. Rueping, S.: Ranking interesting subgroups. In: Proceedings of the 26th International Confer-
ence on Machine Learning (ICML 2009), pp. 913-920 (2009)
13. Shen, L., Joshi, A.K.: Ranking and reranking with perceptron. Journal of Machine Learning
60 (1-3), 73-96 (2005)
Search WWH ::

Custom Search