IS 2013 - IEEE Intelligent Systems Special Issue on Transfer Learning in Web and Social-Network Mining

Call for Papers (http://www.computer.org/portal/web/computingnow/is...)
IEEE Intelligent Systems
Special Issue on Transfer Learning in Web and Social-Network Mining
Publication: May/June 2013
Submissions due for review: 1 September 2012
In the field of information retrieval and Web mining, more and more
learning tasks can easily acquire multiple datasets from various
domains. For example, many of today's recommendation tasks are
starting to leverage multiple types of user data from different
domains, such as users' browsing-history data, shopping-record data,
and social-network (SN) data. At the same time, the need for knowledge
transfer is increasingly evident as many new datasets, or parts of
data, are only very sparsely annotated.
Different from traditional single-domain learning problems (which are
based on the assumption that training and test data are drawn from
identical distributions), transfer learning problems are built on
multiple-domain data that may have different degrees of relatedness to
target tasks. This offers an opportunity for different related
applications to help one another acquire knowledge. To better leverage
multiple-domain data, the mining and transferring of shared knowledge
across multiple domains is likely to become a crucial step in
information retrieval (IR), recommendation, and Web and SN mining in
the future.
This special issue is dedicated to transfer learning for IR and Web
mining. It will bring together researchers from information retrieval
and machine learning, collaborative recommendations, natural language
processing (NLP), social networks, and other areas of computer and
information science who are working on or interested in this area.
There are many challenging research issues to explore. They fallmainly
into two categories:
+ general transfer-learning methods in various multi-domain data types
that arise in IR, recommendation, and Web mining (examples include
heterogeneous, structured, or stream data types), and their related
foundations and theories; and
+ specific transfer-learning methods for various important IR,
collaborative filtering (CF), and Web mining tasks (examples include
transfer learning for ranking, recommendation, NLP, social network
analysis, user profiling, micro-blogging applications, and information
extraction, as well as other novel applications built on multiple
domain data).
This issue will provide a forum for these researchers to identify
issues and challenges, share their latest results, express a diverse
range of opinions about this topic, and discuss future directions. We
believe this issue will become an important milestone in the
development of this new area of IR and Web and SN mining.
*Submission Guidelines*
Submissions should not only describe technical research but also show
its benefits in terms of sustainability. A special emphasis will be
given to research that has matured beyond the design, laboratory, or
simulation stage and that reports experiences and lessons learned from
deployment in the field.
As distributed AI for sustainability involves (often rather
disruptive) sociotechnical innovations, we also welcome
interdisciplinary consideration of social and economic aspects, such
as new sustainable business models, customer service value
propositions, market innovations, incentives for adoption,
interoperability and standardization issues, and regulatory aspects.
Submissions should be 3,000 to 5,400 words (counting a standard figure
or table as 200 words) and should follow IEEE Intelligent Systems
style and presentation guidelines (www.computer.org/intelligent/
author). The manuscripts cannot have been published or be currently
submitted for publication elsewhere.
We strongly encourage submissions that include audio, video, and
community content, which will be featured on the IEEE Computer Society
Web site along with the accepted papers.
*Further Resources*
+ Recent publications on the topic of transfer learning at top
conferences: http://www1.i2r.a-star.edu.sg/~jspan/conferenceTL....
+ "A Transfer Learning Survey" by Sinno Jialin Pan and Qiang Yang,
IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10,
Oct. 2010; http://www1.i2r.a-star.edu.sg/~jspan/publications/....
+ "Transfer Learning Resources," including dataset and code
repositories; www.cse.ust.hk/TL/index.html
+ Recent workshops and symposia:
- "Workshop on Unsupervised and Transfer Learning";
http://clopinet.com/isabelle/Projects/ICML2011
- 25th Annual ConferenceonNeural Information Processing Systems;
http://nips.cc/Conferences/2009/Program/event.php?...
*Guest Editors*
+ Deepak Agarwal, Yahoo! Labs
+ Karsten Borgwardt, Max Planck Insts. & Eberhard Karls Univ.
Tuebingen
+ Yi Chang, Yahoo! Labs
+ Bo Long, Yahoo! Labs
+ Qiang Yang, Hong Kong University of Science and Technology
*Questions?*
+ Information about the special issue's focus: contact Qiang Yang at
qyang-AT-cse.ust.hk (include the keyword "IEEE IS: Transfer Learning" in
the subject line)
+ General author guidelines: www.computer.org/intelligent/author
+ Submission details: contact intelligent-AT-computer.org
+ To submit an article: https://mc.manuscriptcentral.com/is-cs (log
in, then select "Special Issue on Transfer Learning")