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 λ-MART models and provide a faster convergence speed.
|Data di pubblicazione:||2018|
|Titolo:||Continuation Methods and Curriculum Learning for Learning to Rank|
|Titolo del libro:||CIKM '18: Proceedings of the The 27th ACM International Conference on Information and Knowledge Management|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1145/3269206.3269239|
|Appare nelle tipologie:||4.1 Articolo in Atti di convegno|