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LEARning Next gEneration Rankers (LEARNER 2017)

Contributo in Atti di convegno
Data di Pubblicazione:
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.
Tipologia CRIS:
04.01 Contributo in Atti di convegno
Keywords:
Learning to rank
Elenco autori:
Lucchese, Claudio; Perego, Raffaele
Autori di Ateneo:
PEREGO RAFFAELE
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/333421
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URL

http://doi.acm.org/10.1145/3121050.3121110
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