Boosting learning to rank with user dynamics and continuation methods

TY - JOUR

T1 - Boosting learning to rank with user dynamics and continuation methods

AU - Ferro, Nicola

AU - Lucchese, Claudio

AU - Maistro, Maria

AU - Perego, Raffaele

PY - 2020/12/1

Y1 - 2020/12/1

N2 - 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.

AB - 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.

KW - Continuation methods

KW - Learning to rank

KW - User dynamics

UR - http://www.scopus.com/inward/record.url?scp=85074788793&partnerID=8YFLogxK

U2 - 10.1007/s10791-019-09366-9

DO - 10.1007/s10791-019-09366-9

M3 - Journal article

AN - SCOPUS:85074788793

SN - 1386-4564

JO - Information Retrieval Journal

JF - Information Retrieval Journal

ER -