Boosting learning to rank with user dynamics and continuation methods
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Boosting learning to rank with user dynamics and continuation methods. / Ferro, Nicola; Lucchese, Claudio; Maistro, Maria; Perego, Raffaele.
I: Information Retrieval Journal, Bind 23, 2020, s. 528–554.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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
Y1 - 2020
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
U2 - 10.1007/s10791-019-09366-9
DO - 10.1007/s10791-019-09366-9
M3 - Journal article
AN - SCOPUS:85074788793
VL - 23
SP - 528
EP - 554
JO - Information Retrieval
JF - Information Retrieval
SN - 1386-4564
ER -
ID: 230562117