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Extreme Multilabel Classification for Social Media Chairs' Welcome and Organization

Published: 23 April 2018 Publication History

Abstract

Extreme Multilabel Classification (XMLC) is a very active and rapidly growing research area that deals with the problem of labeling an item with a small set of tags out of an extremely large number of potential tags. Applications include content understanding, document tagging, image tagging, biological sequence tagging, recommendation, etc. While the difficulty and the potential applications of XMLC are well understood in the core machine learning community, to the best of our knowledge, XMLC has not made inroads in the field of Information Retrieval (IR) and related areas. The aim of this workshop is to bring researchers from academia and industry in order to further advance this very exciting field and come up with potential applications of XMLC in new areas.

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cover image ACM Other conferences
WWW '18: Companion Proceedings of the The Web Conference 2018
April 2018
2023 pages
ISBN:9781450356404
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • IW3C2: International World Wide Web Conference Committee

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

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Author Tags

  1. extreme classification
  2. multiclass classification
  3. multilabel classification

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  • Research-article

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WWW '18
Sponsor:
  • IW3C2
WWW '18: The Web Conference 2018
April 23 - 27, 2018
Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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