[Topic-models] First CFP: NIPS2010 Workshop on Machine Learning for Social Computing

Zenglin xu218 at purdue.edu
Thu Sep 9 17:51:40 EDT 2010


------ Apologies if you receive multiple copies

*NIPS2010 Workshop -- Machine Learning for Social Computing*
Website: http://mlg.cs.purdue.edu/doku.php?id=mlsc2010


*December 11th, 2010, in conjunction with NIPS 2010
Whistler, BC, Canada*

======= *Good News* =======

We are glad to declare that the proceedings of the workshop will appear in
the JMLR Workshop and Conference Proceedings
series<http://jmlr.csail.mit.edu/proceedings/>
!

Follow us at learning4social at twitter <http://twitter.com/learning4social>

======= *Background *=======

Social computing aims to support the online social behavior through
computational methods. The explosion of the Web has created and been
creating social interactions and social contexts through the use of
software, services and technologies, such as blogs, microblogs (Tweets),
wikis, social network services, social bookmarking, social news, multimedia
sharing sites, online auctions, reputation systems, and so on. Analyzing the
information underneath the social interactions and social context, e.g.,
community detection, opinion mining, trend prediction, anomaly detection,
product recommendation, expert finding, social ranking, information
visualization, will benefit both of information providers and information
consumers in the application areas of social sciences, economics,
psychologies and computer sciences. However, the large volumes of
user-generated contents and the complex structures among users and related
entities require effective modeling methods and efficient solving
algorithms, which therefore bring challenges to advanced techniques in
machine learning. There are three major concerns:

  1. How to effectively and accurately model the related task as a learning
problem?

  2. How to construct efficient and scalable algorithms to solve the
learning task?

  3. How to fully explore and exploit human computation?

======= *Goals *=======

This workshop aims to bring together researchers and practitioners
interested in this area to share their perspectives, identify the challenges
and opportunities, and discuss future research/application directions
through invited talks, panel discussion, and oral/poster presentations.


======= *Topics of Interest* ======

Topics of interest include, but are not limited to:

   *      Communities discovery and analysis in social networks
   *      Sentiment analysis and opinion mining
   *      Topic detection in instant message systems
   *      Fusion of information from multiple blogs, rating systems, and
social networks
   *      Classification and clustering of blogs, tweets based on the
content and link structure
   *      Extraction and visualization of network structures and user
relationships
   *      Trend prediction and dynamics of social networks
   *      Authority identification and influence measurement in social
networks
   *      Collaborative filtering and recommendations systems
   *      Temporal analysis on social network topologies
   *      Social ranking and social tagging
   *      Anomaly detection in social networks
   *      Personalization for search and social interaction
   *      Scalable algorithms dealing with large size of blogosphere and
networks
   *      Statistics for Social Science
   *      Human computation and social games
   *      Privacy protection in social networks
   *      Online advertising
   *      Graph mining algorithms
   *      Large scale algorithms
   *      Parallel or distributed learning algorithms
   *      Online learning algorithms

We invite papers solving the problems in social computing using machine
learning methods, such as statistical methods, graphical models, graph
mining methods, matrix factorization, learning to rank, optimization,
temporal analysis methods, information visualization methods, transfer
learning, and others.


======= *Organizing Committee *=======

Zenglin Xu <http://www.cs.purdue.edu/%7Exu218>, Purdue University
Irwin King <http://www.cse.cuhk.edu.hk/%7Eking/>, The Chinese University of
Hong Kong
Shenghuo Zhu <http://www.cs.rochester.edu/%7Ezsh/>, NEC Labs of America
Alan Qi <http://www.cs.purdue.edu/homes/alanqi>, Purdue University
Rong Yan <http://www.cs.cmu.edu/%7Eyanrong/>, Facebook
John Yen <http://ist.psu.edu/yen/>, Penn State University

======= Program Committee (Tentative) =======

*Brian Davison*, Lehigh University, US
*Hongbo Deng*, University of Illinois at Urbana-Champaign, US
*Tina Eliassi-Rad*, Rutgers University, US
*Bin Gao*, Microsoft Research Asia, China
*Daniel Gatica-Perez*, Idiap research institute, Switzerland
*Lise Getoor*, University of Maryland, College Park, US
*Hao Ma*, The Chinese University of Hong Kong, HK
*Alejandro Jaimes*, Yahoo! Research Europe and Latin America, Spain
*Nick Koudas*, University of Toronto, Canada
*Kristina Lerman*, University of Southern California, US
*Ee-Peng Lim*, Singapore Management University, Singapore
*Huan Liu*, Arizona State University, US
*Sebastian Michel*, Saarland University and MPI Informatics, Germany
*Mohammad Mahdian*, Yahoo! Labs, US
*Jennifer Neville*, Purdue University, US
*Steffen Rendle*, University of Hildesheim, Germany Vikas Sindhwani, IBM
Watson Research Center, US
*Jie Tang*, Tsinghua University, China
*Lei Tang*, Yahoo! Labs, US
*Volker Tresp*, Siemens, Germany
*Laurence T. Yang*, St Francis Xavier University, Canada
*Qiang Yang*, Hong Kong University of Science and Technology, HK
*Kai Yu*, NEC Labs America, US
*Feida Zhu*, Singapore Management University, Singapore

======= *Invited Speakers* (Tentative) =======

Lee Giles <http://clgiles.ist.psu.edu/>, Penn State University


======= *Important Dates* =======

   *      Submission: *23:59 PDT, October 11th, 2010*
   *      Acceptation Notification: *November 1st, 2010*
   *      Camera Ready: *November 15th, 2010*
   *      Workshop: *December 11th, 2010
*
======= *Submission Instructions* =======

Submission Site: Microsoft CMT for Workshop of Machine Learning for Social
Computing 2010 <https://cmt.research.microsoft.com/MLSC2010>.

All submissions must be in pdf format. Papers are limited to maximum six
pages, including figures and tables, in the NIPS style (which can be
obtained from http://nips.cc/PaperInformation/StyleFiles).

The review process is double blind, so do not include any author
information.
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