Call for Participation
August 1st, 1999
"Machine Learning for Information Filtering"
at IJCAI 99
The enormous growth of on-line information and electronic
commerce has brought about a comparable growth in research on methods for
automatically organizing and personalizing information. The "information
filtering" task has simultaneously emerged as an active research topic
in several disciplines, including information retrieval, human computer
interaction, natural language processing, and machine learning. The
information filtering task manifests itself in many theoretically challenging
and commercially important applications, such as electronic commerce and
marketing, search engines, information push applications, browsing assistants,
and adaptive Web sites.
The goal of this workshop is to bring together researchers
working on information filtering from many subfields of AI, while emphasizing
the machine learning techniques and algorithms many of these subfields
share. These techniques include
Besides these topics, the workshop covers all theoretical
and methodological issues concerning information filtering. Submissions
describing innovative applications of information filtering are also encouraged.
By bringing together industrial representatives with researchers, the workshop
text classification methods (probabilistic methods, support
vector machines, first order methods, use of unlabeled data, etc.)
collaborative filtering methods (use of complex user and
object profiles (e.g. citation structure), novel clustering models and
other methods for learning user preferences (learning orderings,
combinations of approaches and multi-strategy learning
representational issues (knowledge representation, NLP techniques,
representing interest, representing information objects, feature
selection, term weighting, data transformation, latent semantic indexing,
clustering methods (similarity measures, mixture models,
formal models and theory
handling different media (text, images, sound, etc.)
show how problems from industry present new research issues.
identify ways in which research results may be put in more
widespread practice in an industrial setting.
- Prof. Oren Etzioni (Univ. of. Washington)
- Jan Pedersen (Director, Advanced Technology, InfoSeek Corp.)
|May 3, 1999
||Notification of acceptance
|May 24, 1999
||Camera-ready copy due
|August 1, 1999
The workshop will be one full day, including invited talks,
paper presentations, poster presentations, and numerous opportunities for
discussion. Depending on submissions, there will be joint sessions with
the workshop "Text Mining: Foundations, Techniques and Applications" on
topics of common interest. Participation in the workshop is limited according
to IJCAI regulations. All workshop participants have to register for the
IJCAI conference. The working notes of the workshop will be published online.
Those interested in making a presentation should submit a
full paper electronically either as a Postscript or PDF to email@example.com.
The first page of submitted papers should include: title, author names
and affiliations, a brief abstract. It should also name a designated contact
person with his or her postal address, electronic mail address, telephone
and fax number. Submissions should not exceed 8 pages (including the title page) according to
the IJCAI formatting
instructions and should be printable on 8.5" x 11" or A4 paper.
Those interested in participating in the workshop, but
not submitting a paper, should submit a one-page abstract of their research
interests in learning methods for information filtering.
INFORMATION FOR AUTHORS
Authors with contributions accepted for oral presentation
will have 20 minutes for their talk (including time for questions).
Authors with poster presentations will have 5 minutes for a short summary
of their poster. The size of the poster boards is 96cm x 242cm. An overhead
projector, a data projector, and a PC running Windows and Netscape will
be available. If you need any other equipment, please let us know as soon
Last modified November 30th, 1998 by Thorsten