Report on LEARNER 2017: 1st International Workshop on LEARning Next gEneration Rankers
Abstract
The LEARNER workshop was co-located with the "third ACM International Conference on the Theory of Information Retrieval", (ICTIR 2017). The goal of the workshop was to foster investigation on novel Learning-to-Rank algorithms, on their evaluation, on dataset creation and curation, and on domain specific applications of Learning-to-Rank. The half-day workshop hosted eight paper presentations and two invited talks: the first by Craig Macdonald on "Hypothesis Testing for Risk-Sensitive Evaluation and Learning to Rank in Web Search" and the second by Djoerd Hiemstra entitled "Ranking Learning-to-Rank Methods". Detailed information is available at http://learner2017.dei.unipd.it/.
References
[1]
Brian Brost. Multileaving for Online Evaluation of Rankers. In Ferro et al. {4}.
[2]
Olivier Chapelle, Yi Chang, and Tie-Yan Liu. Future directions in learning to rank. In Proceedings of the 2010 International Conference on Yahoo! Learning to Rank Challenge - Volume 14, Proceedings of Machine Learning Research, Vol. 14, pages 91--100. PMLR, 2011. URL http://dl.acm.org/citation.cfm?id=3045754.3045764.
[3]
Arpita Das, Saurabh Shrivastava, Manoj Chinnakotla, Prateek Agrawal, and Sandeep Sahoo. Discovery and Promotion of Subtopic Level High Quality Domains for Programming Queries in Web Search. In Ferro et al. {4}.
[4]
N. Ferro, C. Lucchese, M. Maistro, and R. Perego, editors. 1st International Workshop on LEARning Next gEneration Rankers (LEARNER 2017), 2017. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073.
[5]
Nicola Ferro, Claudio Lucchese, Maria Maistro, and Raffaele Perego. LEARning Next gEneration Rankers (LEARNER 2017). In J. Kamps, E. Kanoulas, M. de Rijke, H. Fang, and E. Yilmaz, editors, Proc. 3rd ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR 2017), pages 331--332. ACM Press, New York, USA, 2017.
[6]
Nicola Ferro, Paolo Picello, and Gianmaria Silvello. A Software Library for Conducting Large Scale Experiments on Learning to Rank Algorithms. In Ferro et al. {4}.
[7]
Darío Garigliotti and Krisztian Balog. Learning to Rank Target Types for Entity-Bearing Queries. In Ferro et al. {4}.
[8]
Djoerd Hiemstra, Niek Tax, and Sander Bockting. Ranking Learning-to-Rank Methods. In Ferro et al. {4}.
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Rolf Jagerman, Harrie Oosterhuis, and Maarten de Rijke. Qery-level Ranker Specialization. In Ferro et al. {4}.
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Or Levi. Online Learning of a Ranking Formula for Revenue and Advertiser ROI Optimization. In Ferro et al. {4}.
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Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, and Salvatore Trani. The Impact of Negative Samples on Learning to Rank. In Ferro et al. {4}.
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December 2017
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 February 2018
Published in SIGIR Volume 51, Issue 3
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