Information Technology Reference
In-Depth Information
13.2 Click-through data captures informative user behaviors. Enumerate other uses
of the click-through data in addition to ground-truth label mining.
13.3 Active learning has been well studied for classification. Please analyze the
unique properties of ranking that make these previous techniques not fully
applicable to ranking.
13.4 In this chapter, we have introduced how to select documents per query for
more efficient labeling and more effective learning. Actually it is also mean-
ingful to select queries for both labeling and learning. Design a query se-
lection algorithm and use the LETOR benchmark datasets to test its perfor-
mance.
References
1. Agichtein, E., Brill, E., Dumais, S.T., Ragno, R.: Learning user interaction models for pre-
dicting web search result preferences. In: Proceedings of the 29th Annual International ACM
SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2006),
pp. 3-10 (2006)
2. Aslam, J.A., Kanoulas, E., Pavlu, V., Savev, S., Yilmaz, E.: Document selection methodolo-
gies for efficient and effective learning-to-rank. In: Proceedings of the 32nd Annual Interna-
tional ACM SIGIR Conference on Research and Development in Information Retrieval (SI-
GIR 2009), pp. 468-475 (2009)
3. Aslam, J.A., Pavlu, V., Savell, R.: A unified model for metasearch and the efficient evalu-
ation of retrieval systems via the hedge algorithm. In: Proceedings of the 26th Annual In-
ternational ACM SIGIR Conference on Research and Development in Information Retrieval
(SIGIR 2003), pp. 393-394 (2003)
4. 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)
5. 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)
6. Carterette, B., Allan, J., Sitaraman, R.: Minimal test collections for retrieval evaluation. In:
Proceedings of the 29th Annual International ACM SIGIR Conference on Research and De-
velopment in Information Retrieval (SIGIR 2006), pp. 268-275 (2006)
7. Chapelle, O., Zhang, Y.: A dynamic Bayesian network click model for web search ranking.
In: Proceedings of the 18th International Conference on World Wide Web (WWW 2009),
pp. 1-10 (2009)
8. Chen, W., Liu, T.Y., Ma, Z.M.: Two-layer generalization analysis for ranking using
rademacher average. 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. 370-378
(2011)
9. Cossock, D., Zhang, T.: Subset ranking using regression. In: Proceedings of the 19th Annual
Conference on Learning Theory (COLT 2006), pp. 605-619 (2006)
10. Craswell, N., Zoeter, O., Taylor, M., Ramsey, B.: An experimental comparison of click
position-bias models. In: Proceedings of the 1st International Conference on Web Search and
Web Data Mining (WSDM 2008), pp. 87-94 (2008)
11. Donmez, P., Svore, K.M., Burges, C.J.C.: On the local optimality of lambdarank. In: Proceed-
ings of the 32nd Annual International ACM SIGIR Conference on Research and Development
in Information Retrieval (SIGIR 2009), pp. 460-467 (2009)
