Standard
Multileaving for online evaluation of rankers. / Brost, Brian.
Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017). ed. / Nicola Ferro; Claudio Lucchese; Maria Maistro; Raffaele Perego. CEUR-WS.org, 2017. (CEUR Workshop Proceedings, Vol. 2007).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Brost, B 2017,
Multileaving for online evaluation of rankers. in N Ferro, C Lucchese, M Maistro & R Perego (eds),
Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017). CEUR-WS.org, CEUR Workshop Proceedings, vol. 2007, 1st International Workshop on LEARning Next gEneration Rankers, Amsterdam, Netherlands,
01/10/2017. <
http://ceur-ws.org/Vol-2007/LEARNER2017_short_2.pdf>
APA
Brost, B. (2017).
Multileaving for online evaluation of rankers. In N. Ferro, C. Lucchese, M. Maistro, & R. Perego (Eds.),
Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017) CEUR-WS.org. CEUR Workshop Proceedings Vol. 2007
http://ceur-ws.org/Vol-2007/LEARNER2017_short_2.pdfVancouver
Brost B. Multileaving for online evaluation of rankers. In Ferro N, Lucchese C, Maistro M, Perego R, editors, Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017). CEUR-WS.org. 2017. (CEUR Workshop Proceedings, Vol. 2007).
Author
Brost, Brian. / Multileaving for online evaluation of rankers. Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017). editor / Nicola Ferro ; Claudio Lucchese ; Maria Maistro ; Raffaele Perego. CEUR-WS.org, 2017. (CEUR Workshop Proceedings, Vol. 2007).
Bibtex
@inproceedings{b878fd5d1fd6428fb0c30f74b94b139b,
title = "Multileaving for online evaluation of rankers",
abstract = "In online learning to rank we are faced with a tradeoff between exploring new, potentially superior rankers, and exploiting our preexisting knowledge of what rankers have performed well in the past. Multileaving methods offer an attractive approach to this problem since they can efficiently use online feedback to simultaneously evaluate a potentially arbitrary number of rankers. In this talk we discuss some of the main challenges in multileaving, and discuss promising areas for future research.",
author = "Brian Brost",
year = "2017",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "CEUR-WS.org",
editor = "Nicola Ferro and Claudio Lucchese and Maria Maistro and Raffaele Perego",
booktitle = "Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017)",
note = "null ; Conference date: 01-10-2017 Through 01-10-2017",
}
RIS
TY - GEN
T1 - Multileaving for online evaluation of rankers
AU - Brost, Brian
N1 - Conference code: 1
PY - 2017
Y1 - 2017
N2 - In online learning to rank we are faced with a tradeoff between exploring new, potentially superior rankers, and exploiting our preexisting knowledge of what rankers have performed well in the past. Multileaving methods offer an attractive approach to this problem since they can efficiently use online feedback to simultaneously evaluate a potentially arbitrary number of rankers. In this talk we discuss some of the main challenges in multileaving, and discuss promising areas for future research.
AB - In online learning to rank we are faced with a tradeoff between exploring new, potentially superior rankers, and exploiting our preexisting knowledge of what rankers have performed well in the past. Multileaving methods offer an attractive approach to this problem since they can efficiently use online feedback to simultaneously evaluate a potentially arbitrary number of rankers. In this talk we discuss some of the main challenges in multileaving, and discuss promising areas for future research.
UR - http://www.scopus.com/inward/record.url?scp=85038836886&partnerID=8YFLogxK
M3 - Article in proceedings
AN - SCOPUS:85038836886
T3 - CEUR Workshop Proceedings
BT - Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017)
A2 - Ferro, Nicola
A2 - Lucchese, Claudio
A2 - Maistro, Maria
A2 - Perego, Raffaele
PB - CEUR-WS.org
Y2 - 1 October 2017 through 1 October 2017
ER -
