LEARning Next gEneration Rankers (LEARNER 2017)

Nicola Ferro, Claudio Lucchese, Maria Maistro, Raffaele Perego

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.

Original languageEnglish
Title of host publicationICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval
Number of pages2
PublisherAssociation for Computing Machinery, Inc.
Publication date1 Oct 2017
Pages331-332
ISBN (Electronic)9781450344906
DOIs
Publication statusPublished - 1 Oct 2017
Externally publishedYes
Event7th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2017 - Amsterdam, Netherlands
Duration: 1 Oct 20174 Oct 2017

Conference

Conference7th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2017
Country/TerritoryNetherlands
CityAmsterdam
Period01/10/201704/10/2017
SponsorACM Special Interest Group on Information Retrieval (SIGIR)

Keywords

  • Datasets
  • Evaluation
  • Learning to rank
  • User behaviour

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