Dear all,

We are happy to announce Extreme Multilabel Classification for Social Media
workshop organized at WWW 2018. XMLC is a very active and rapidly growing
research area that deals with the problem of labeling an item with a set of
tags out of an extremely large number of potential tags. 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 to further advance this very exciting field and come up with
potential applications of XMLC in new areas. Our pleasure to have some of
the most prominent experts of this field as speaker of our workshop. The
tentative program is of high quality with the following speakers, without
being exhaustive:


- Mouhamadou Moustapha Cisse: Deep Extreme Classification: From head to tail
- Anshumali Shrivastava: Training 100,000 classes on a Single Titan X in 7
hours or 15 minutes with 25 Titan Xs
- Manik Varma: Extremes Classification: Tagging on Wikipedia,
Recommendation on Amazon & Advertising on Bing
- Marius Kloft: Distributed Training of All-in-one Multi-class SVMs
- Lu Tang, Sougata Chaudhuri, Abraham Bagherjeiran and Ling Zhou: Learning
Large Scale Ordinal Ranking Model via Divide-and-Conquer Technique
- Kishaloy Halder, Lahari Poddar and Min-Yen Kan: Cold Start Thread
Recommendation as Extreme Multi-label Classification



If you are going to attend WWW'18, our XMLC workshop is definitely a good
place to stop by, where you can listen some interesting talks, participate
in lively discussions.


The submission site is still open for non-proceedings submission till 15th
of March. Any submission related to XMLC is welcome.

So see you there!

Best regards,
Akshay Soni, Yahoo Research, Sunnyvale
Robert Busa-Fekete, Yahoo Research, New York
Krzysztof Dembczyński, Poznan University of Technology
Aasish Pappu, Yahoo Research, New York
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