"Machine Learning for Information Filtering"
at IJCAI 99
August 1st, 1999
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.)
CALL FOR PARTICIPATION
Last modified November 30th, 1998 by Thorsten