Abstract
In this chapter, we introduce semi-supervised learning for ranking. The motivation of this topic comes from the fact that we can always collect a large number of unlabeled documents or queries at a low cost. It would be very helpful if one can leverage such unlabeled data in the learning-to-rank process. In this chapter, we mainly review a transductive approach and an inductive approach to this task, and discuss how to improve these approaches by taking the unique properties of ranking into consideration.
This is a preview of subscription content, log in via an institution to check access.
References
Amini, M.R., Truong, T.V., Goutte, C.: A boosting algorithm for learning bipartite ranking functions with partially labeled data. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 99–106 (2008)
Duh, K., Kirchhoff, K.: Learning to rank with partially-labeled data. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 251–258 (2008)
Freund, Y., Iyer, R., Schapire, R., Singer, Y.: An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4, 933–969 (2003)
Niu, Z.Y., Ji, D.H., Tan, C.L.: Word sense disambiguation using label propagation based semi-supervised learning. In: Proceedings of the 403rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 395–402 (2005)
Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1998), pp. 275–281 (1998)
Robertson, S.E.: Overview of the okapi projects. Journal of Documentation 53(1), 3–7 (1997)
Tong, W., Jin, R.: Semi-supervised learning by mixed label propagation. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI 2007), pp. 651–656 (2007)
Xiaojin Zhu, Z.G.: Learning from labeled and unlabeled data with label propagation. Ph.D. thesis, Carnegie Mellon University (2002)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Liu, TY. (2011). Semi-supervised Ranking. In: Learning to Rank for Information Retrieval. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14267-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-14267-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14266-6
Online ISBN: 978-3-642-14267-3
eBook Packages: Computer ScienceComputer Science (R0)