A cross-benchmark comparison of 87 learning to rank methods

Author:

Tax Niek,Bockting Sander,Hiemstra Djoerd

Publisher

Elsevier BV

Subject

Library and Information Sciences,Management Science and Operations Research,Computer Science Applications,Media Technology,Information Systems

Reference155 articles.

1. Acharyya, S., Koyejo, O., & Ghosh, J. (2012). Learning to rank with Bregman divergences and monotone retargeting. In Proceedings of the 28th conference on uncertainty in artificial intelligence (UAI).

2. Adams, R. P., & Zemel, R. S. (2011). Ranking via Sinkhorn Propagation. Available from: .

3. Agarwal, S., & Collins, M. (2010). Maximum Margin Ranking Algorithms for Information Retrieval. In Proceedings of the 32nd European conference on information retrieval research (ECIR) (pp. 332–343).

4. Ah-Pine, J. (2008). Data fusion in information retrieval using consensus aggregation operators. In Proceedings of the IEEE/WIC/ACM international conference on Web intelligence and intelligent agent technology (WI-IAT) (Vol. 1, pp. 662–668).

5. Wcl2r: A benchmark collection for learning to rank research with clickthrough data;Alcântara;Journal of Information and Data Management,2010

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