2019
Journal article  Open Access

Boosting learning to rank with user dynamics and continuation methods

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

Settore INF/01 - Informatica  Library and Information Sciences  User dynamics  Information Systems  Learning to rank  Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni  Continuation methods 

Learning to rank (LtR) techniques leverage assessed samples of query-document relevance to learn effective ranking functions able to exploit the noisy signals hidden in the features used to represent queries and documents. In this paper we explore how to enhance the state-of-the-art LambdaMart LtR algorithm by integrating in the training process an explicit knowledge of the underlying user-interaction model and the possibility of targeting different objective functions that can effectively drive the algorithm towards promising areas of the search space. We enrich the iterative process followed by the learning algorithm in two ways: (1) by considering complex query-based user dynamics instead than simply discounting the gain by the rank position; (2) by designing a learning path across different loss functions that can capture different signals in the training data. Our extensive experiments, conducted on publicly available datasets, show that the proposed solution permits to improve various ranking quality measures by statistically significant margins.

Source: Information retrieval (Boston) 23 (2019): 528–554. doi:10.1007/s10791-019-09366-9

Publisher: Kluwer Academic Publishers, Boston , Stati Uniti d'America


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BibTeX entry
@article{oai:it.cnr:prodotti:416220,
	title = {Boosting learning to rank with user dynamics and continuation methods},
	author = {Ferro N. and Lucchese C. and Maistro M. and Perego R.},
	publisher = {Kluwer Academic Publishers, Boston , Stati Uniti d'America},
	doi = {10.1007/s10791-019-09366-9},
	journal = {Information retrieval (Boston)},
	volume = {23},
	pages = {528–554},
	year = {2019}
}