On Including the user dynamic in learning to rank

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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. p. 1041-1044.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Ferro, N, Lucchese, C, Maistro, M & Perego, R 2017, On Including the user dynamic in learning to rank. in SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc., pp. 1041-1044, 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017, Tokyo, Shinjuku, Japan, 07/08/2017. https://doi.org/10.1145/3077136.3080714

APA

Ferro, N., Lucchese, C., Maistro, M., & Perego, R. (2017). On Including the user dynamic in learning to rank. In SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1041-1044). Association for Computing Machinery, Inc.. https://doi.org/10.1145/3077136.3080714

Vancouver

Ferro N, Lucchese C, Maistro M, Perego R. On Including the user dynamic in learning to rank. In SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc. 2017. p. 1041-1044 https://doi.org/10.1145/3077136.3080714

Author

Ferro, Nicola ; Lucchese, Claudio ; Maistro, Maria ; Perego, Raffaele. / On Including the user dynamic in learning to rank. SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc., 2017. pp. 1041-1044

Bibtex

@inproceedings{8ba492b17ae6446f81ecee093d74f940,
title = "On Including the user dynamic in learning to rank",
abstract = "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.",
keywords = "LambdaMart, Learning to rank, User dynamic",
author = "Nicola Ferro and Claudio Lucchese and Maria Maistro and Raffaele Perego",
year = "2017",
month = aug,
day = "7",
doi = "10.1145/3077136.3080714",
language = "English",
pages = "1041--1044",
booktitle = "SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval",
publisher = "Association for Computing Machinery, Inc.",
note = "40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 ; Conference date: 07-08-2017 Through 11-08-2017",

}

RIS

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