LUC 2012

LUC 2012
International Workshop on Learning from User-generated Content
http://www.cp.jku.at/conferences/LUC2012
In conjunction with the
29th International Conference on Machine Learning (ICML 2012)
Edinburgh, UK, June 26–July 1, 2012

The International Workshop on Learning from User-generated Content (LUC)
serves as a forum for theoretical and practical discussions of cutting
edge research on machine learning technologies for multimedia information
retrieval. Topics covered include all aspects of machine learning with a
relation to social media and user-generated content, in particular,
(social) web mining, multimedia information extraction, retrieval, and
recommendation as well as mobile applications and services that make use
of machine learning and Web technology. Submissions addressing concrete
implementations of systems and services by both academic institutions and
industrial companies are also welcome.

Call for Papers:
While the amount of user-generated content has been skyrocketing since the
advent of social media and social networks, intelligent approaches to process
and make sense of these huge masses of data produced by over a billion users
are rather rare so far.
Hence, we solicit innovative technical papers with a focus on user-generated
content and addressing problems in the fields of machine learning,
multimedia,
or information retrieval. Also contributions that combine two or more of
these fields are highly welcome.
We invite authors to submit regular technical papers of up to 8 pages
as well as short position or demo papers of 2-4 pages .
Regardless of their category, submissions must follow the ACM author
guidelines. Paper submissions must be original and not submitted to or
accepted by any other conference or journal. All submissions to this workshop
will be peer-reviewed by at least three Program Committee members.
The review process will be double-blind.

Submissions tackling, for example, one of the following challenges are
highly welcome:

Explore the usage of several types of social data, including ratings,
reviews, tags, comments, hyperlinks, geo-located data, linked data,
multimedia items, and playlists.

Extract from these raw data several types of knowledge (users and data):
relations, user opinions, user preferences, semantic
relationships/description of multimedia objects, sentiment analysis,
community detection

Exploit novel data mining and information retrieval techniques:
expert finding, recommendation computation, similarity evaluation, network
analysis, information visualization, multimedia retrieval, semantic
indexing, evaluation of systems with implicit data from social media,
social and human computation

Define new information search problems:
context-dependent recommendation, definition of key users, identification
of relevant locations, cross-domain multimedia recommendation, games and
multimedia, cross-modal social content analysis

Evaluate the benefits of such techniques:
live users experiments, new off-line evaluation methods

Topics of Interest:
– Social Media and Network Analysis
– Social Media Mining
– Influential User Detection and Analysis
– Information Extraction and Knowledge Harvesting from User-generated Data
– Information Visualization in Social Media
– Multimedia Retrieval
– Tagging and Games with a Purpose
– Semantic Content Analysis and Indexing
– Opinion Mining and Sentiment Analysis
– Large-Scale Similarity Measurement, Scalability Issues and Solutions
– Evaluation, Mining of Ground Truth and Data Collections
– Semantic Web, Linked Data, Ontologies, Semantics and Reasoning
– Novel Machine Learning Algorithms Tailored to Social Media

Organizers/Program Chairs:
Masataka Goto AIST, Tsukuba, Japan
Gert Lanckriet University of California, San Diego, USA
Francesco Ricci Free University of Bozen-Bolzano, Italy
Markus Schedl Johannes Kepler University, Linz, Austria
Julián Urbano University Carlos III of Madrid, Spain

Important Dates:
Paper Submission 07-05-2012
Notification 21-05-2012

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