Document - Continuation methods and curriculum learning for learning to rank

2018

Conference article Open Access

Continuation methods and curriculum learning for learning to rank

Ferro N., Lucchese C., Maistro M., Perego R.

Curriculum learning Lambdamart Learning to rank

In this paper we explore the use of Continuation Methods and Curriculum Learning techniques in the area of Learning to Rank. The basic idea is to design the training process as a learning path across increasingly complex training instances and objective functions. We propose to instantiate continuation methods in Learning to Rank by changing the IR measure to optimize during training, and we present two different curriculum learning strategies to identify easy training examples. Experimental results show that simple continuation methods are more promising than curriculum learning ones since they allow for slightly improving the performance of state-of-the-art ?-MART models and provide a faster convergence speed.

Source: ACM CIKM, pp. 1523–1526, Torino, 22-26/10/2018

Publisher: ACM Press, New York, USA

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@inproceedings{oai:it.cnr:prodotti:416218,
	title = {Continuation methods and curriculum learning for learning to rank},
	author = {Ferro N. and Lucchese C. and Maistro M. and Perego R.},
	publisher = {ACM Press, New York, USA},
	doi = {10.1145/3269206.3269239},
	booktitle = {ACM CIKM, pp. 1523–1526, Torino, 22-26/10/2018},
	year = {2018}
}