Document - LEARning Next gEneration Rankers (LEARNER 2017)

2017

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LEARning Next gEneration Rankers (LEARNER 2017)

Ferro N., Lucchese C., Maistro M., Perego R.

Learning to rank

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.

Source: ICTIR '17 - ACM SIGIR International Conference on Theory of Information Retrieval, pp. 331–332, Amsterdam, The Netherlands, 1-4 October, 2017

Publisher: ACM - Association for Computing Machinery, New York, USA

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@inproceedings{oai:it.cnr:prodotti:381015,
	title = {LEARning Next gEneration Rankers (LEARNER 2017)},
	author = {Ferro N. and Lucchese C. and Maistro M. and Perego R.},
	publisher = {ACM - Association for Computing Machinery, New York, USA},
	doi = {10.1145/3121050.3121110},
	booktitle = {ICTIR '17 - ACM SIGIR International Conference on Theory of Information Retrieval, pp. 331–332, Amsterdam, The Netherlands, 1-4 October, 2017},
	year = {2017}
}