LEARning Next gEneration Rankers (LEARNER 2017)
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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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
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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