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WSDM Tutorials 2019 : Call for Tutorials -- WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining @ Melbourne, Australia, Feb. 11-15, 2019

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Link: http://www.wsdm-conference.org/2019/
 
When Feb 11, 2019 - Feb 15, 2019
Where Melbourne, Australia
Submission Deadline Sep 17, 2018
Notification Due Oct 15, 2018
Categories    information retrieval   web mining   web search   machine learning
 

Call For Papers

Call for Tutorials
WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining
http://www.wsdm-conference.org/2019/

Melbourne, Australia, February 11-15, 2019


We invite proposals for half and full-day tutorials to be held at the 12th Web Search and Data Mining (WSDM) conference. WSDM takes place between February 11 and February 15, 2019 in Melbourne, Australia.

The WSDM 2019 Tutorial Program is intended to disseminate information to conference attendees on recently emerging topics and trends, provide surveys of complementary techniques to those commonly studied at WSDM, and inform industrial practitioners on the state-of-the-art within the field. We encourage submissions of proposals on topics in the general areas of web search, data mining, information retrieval, and social search. WSDM historically has drawn audiences from both academia and industry. This should be taken into consideration in the proposal. Tutorials will be given by experts in the subject matter.



## Submission Guidelines

https://easychair.org/cfp/wsdm2019-tutorials

Submissions should include a cover sheet and an extended abstract. The cover sheet should include the following elements

* Title and length (either half-day, e.g. 3 hours plus breaks, or full day, e.g., 6 hours plus breaks) of the tutorial
* Short abstract (300 words)
* Intended audience (introductory, intermediate, advanced) and prerequisite knowledge or skills required
* Previous offering(s) of the tutorial
* Detailed contact information of all presenters (and indication of the main contact person)
* Brief biography (max. 2 paragraphs) for each presenter, highlighting relevant experience in presenting tutorials, teaching classes, organizing summer schools, etc.

The extended abstract (no longer than 3 pages) should include the following sections

* Motivation
* Brief outline of the topics to be covered
* Relevance to the community and references to related resources (and tutorials at related conferences)
* Format and detailed schedule
* Type of support materials to be supplied to attendees.

Tutorial proposals must be submitted in PDF according to the new ACM format published in the ACM guidelines, selecting the generic "sigconf" sample.


## Schedule

* September 17th, 2018: Tutorial Proposal Deadline
* October 15th, 2018: Tutorial Acceptance Notifications
* February 11-15, 2019: Conference (tutorial day not yet known)


## PC Members (confirmed)

Andrew Trotman
Alessandro Moschitti
Pavel Serdyukov
Yiqun Liu
Bhaskar Mitra
Elena Zheleva
Mounia Lalmas
Rodrygo LT Santos
Jiafeng Guo
Nicola Ferro
Ben Carterette

## Tutorial Chairs

Claudia Hauff, Delft University of Technology
Jeff Dalton, University of Glasgow

You can contact us at:
wsdm2019tutorials@easychair.org

Related Resources

WSDM 2019   WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining
ICCV 2019   International Conference on Computer Vision
WSDM 2018   The 11th ACM International Conference on Web Search and Data Mining
ICML 2019   36th International Conference on Machine Learning
SIGIR 2019   International ACM SIGIR Conference on Research and Development in Information Retrieval
MLDM 2019   15th International Conference on Machine Learning and Data Mining MLDM 2019
WSDM Demonstrations 2019   Call for Demonstrations -- WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining @ Melbourne, Australia, Feb. 11-15, 2019
Task Intelligence - WSDM 2019   Task Intelligence Workshop at WSDM 2019
WSDM 2019   International Workshop on Web Search and Data Mining
ICDMML 2019   【ACM ICPS EI SCOPUS】2019 International Conference on Data Mining and Machine Learning