On Including the user dynamic in learning to rank
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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On Including the user dynamic in learning to rank. / Ferro, Nicola; Lucchese, Claudio; Maistro, Maria; Perego, Raffaele.
SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc., 2017. s. 1041-1044.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - On Including the user dynamic in learning to rank
AU - Ferro, Nicola
AU - Lucchese, Claudio
AU - Maistro, Maria
AU - Perego, Raffaele
PY - 2017/8/7
Y1 - 2017/8/7
N2 - Ranking query results effectively by considering user past behaviour and preferences is a primary concern for IR researchers both in academia and industry. In this context, LtR is widely believed to be the most effective solution to design ranking models that account for user-interaction features that have proved to remarkably impact on IR effectiveness. In this paper, we explore the possibility of integrating the user dynamic directly into the LtR algorithms. Specifically, we model with Markov chains the behaviour of users in scanning a ranked result list and we modify LambdaMart, a state-of-The-Art LtR algorithm, to exploit a new discount loss function calibrated on the proposed Markovian model of user dynamic. We evaluate the performance of the proposed approach on publicly available LtR datasets, finding that the improvements measured over the standard algorithm are statistically significant.
AB - Ranking query results effectively by considering user past behaviour and preferences is a primary concern for IR researchers both in academia and industry. In this context, LtR is widely believed to be the most effective solution to design ranking models that account for user-interaction features that have proved to remarkably impact on IR effectiveness. In this paper, we explore the possibility of integrating the user dynamic directly into the LtR algorithms. Specifically, we model with Markov chains the behaviour of users in scanning a ranked result list and we modify LambdaMart, a state-of-The-Art LtR algorithm, to exploit a new discount loss function calibrated on the proposed Markovian model of user dynamic. We evaluate the performance of the proposed approach on publicly available LtR datasets, finding that the improvements measured over the standard algorithm are statistically significant.
KW - LambdaMart
KW - Learning to rank
KW - User dynamic
UR - http://www.scopus.com/inward/record.url?scp=85029368520&partnerID=8YFLogxK
U2 - 10.1145/3077136.3080714
DO - 10.1145/3077136.3080714
M3 - Article in proceedings
AN - SCOPUS:85029368520
SP - 1041
EP - 1044
BT - SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery, Inc.
T2 - 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
Y2 - 7 August 2017 through 11 August 2017
ER -
ID: 216517264