LEARning Next gEneration Rankers (LEARNER 2017)

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

LEARning Next gEneration Rankers (LEARNER 2017). / Ferro, Nicola; Lucchese, Claudio; Maistro, Maria; Perego, Raffaele.

ICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval. Association for Computing Machinery, Inc., 2017. s. 331-332.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Ferro, N, Lucchese, C, Maistro, M & Perego, R 2017, LEARning Next gEneration Rankers (LEARNER 2017). i ICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval. Association for Computing Machinery, Inc., s. 331-332, 7th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2017, Amsterdam, Holland, 01/10/2017. https://doi.org/10.1145/3121050.3121110

APA

Ferro, N., Lucchese, C., Maistro, M., & Perego, R. (2017). LEARning Next gEneration Rankers (LEARNER 2017). I ICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval (s. 331-332). Association for Computing Machinery, Inc.. https://doi.org/10.1145/3121050.3121110

Vancouver

Ferro N, Lucchese C, Maistro M, Perego R. LEARning Next gEneration Rankers (LEARNER 2017). I ICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval. Association for Computing Machinery, Inc. 2017. s. 331-332 https://doi.org/10.1145/3121050.3121110

Author

Ferro, Nicola ; Lucchese, Claudio ; Maistro, Maria ; Perego, Raffaele. / LEARning Next gEneration Rankers (LEARNER 2017). ICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval. Association for Computing Machinery, Inc., 2017. s. 331-332

Bibtex

@inproceedings{6e62ae03b57b47558516197492e39a45,
title = "LEARning Next gEneration Rankers (LEARNER 2017)",
abstract = "The aim of LEARNER@ICTIR2017 is to investigate new solutions for LtR. In details, we identify some research areas related to LtR which are of actual interest and which have not been fully explored yet. We solicit the submission of position papers on novel LtR algorithms, on evaluation of LtR algorithms, on dataset creation and curation, and on domain specific applications of LtR. LEARNER@ICTIR2017 will be a gathering of academic people interested in IR, ML and related application areas. We believe that the proposed workshop is relevant to ICTIR since we look for novel contributions to LtR focused on foundational and conceptual aspects, which need to be properly framed and modeled.",
keywords = "Datasets, Evaluation, Learning to rank, User behaviour",
author = "Nicola Ferro and Claudio Lucchese and Maria Maistro and Raffaele Perego",
year = "2017",
month = oct,
day = "1",
doi = "10.1145/3121050.3121110",
language = "English",
pages = "331--332",
booktitle = "ICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval",
publisher = "Association for Computing Machinery, Inc.",
note = "7th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2017 ; Conference date: 01-10-2017 Through 04-10-2017",

}

RIS

TY - GEN

T1 - LEARning Next gEneration Rankers (LEARNER 2017)

AU - Ferro, Nicola

AU - Lucchese, Claudio

AU - Maistro, Maria

AU - Perego, Raffaele

PY - 2017/10/1

Y1 - 2017/10/1

N2 - The aim of LEARNER@ICTIR2017 is to investigate new solutions for LtR. In details, we identify some research areas related to LtR which are of actual interest and which have not been fully explored yet. We solicit the submission of position papers on novel LtR algorithms, on evaluation of LtR algorithms, on dataset creation and curation, and on domain specific applications of LtR. LEARNER@ICTIR2017 will be a gathering of academic people interested in IR, ML and related application areas. We believe that the proposed workshop is relevant to ICTIR since we look for novel contributions to LtR focused on foundational and conceptual aspects, which need to be properly framed and modeled.

AB - The aim of LEARNER@ICTIR2017 is to investigate new solutions for LtR. In details, we identify some research areas related to LtR which are of actual interest and which have not been fully explored yet. We solicit the submission of position papers on novel LtR algorithms, on evaluation of LtR algorithms, on dataset creation and curation, and on domain specific applications of LtR. LEARNER@ICTIR2017 will be a gathering of academic people interested in IR, ML and related application areas. We believe that the proposed workshop is relevant to ICTIR since we look for novel contributions to LtR focused on foundational and conceptual aspects, which need to be properly framed and modeled.

KW - Datasets

KW - Evaluation

KW - Learning to rank

KW - User behaviour

UR - http://www.scopus.com/inward/record.url?scp=85033234672&partnerID=8YFLogxK

U2 - 10.1145/3121050.3121110

DO - 10.1145/3121050.3121110

M3 - Article in proceedings

AN - SCOPUS:85033234672

SP - 331

EP - 332

BT - ICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval

PB - Association for Computing Machinery, Inc.

T2 - 7th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2017

Y2 - 1 October 2017 through 4 October 2017

ER -

ID: 216517201