Call for participation Machine Learning for User Modeling UM-2001 Workshop

Machine Learning is concerned with the formation of models from observations. Hence, learning algorithms are promising candidates for user model acquisition. Additionally, the theory revision techniques provided by machine learning approaches may prove helpful in user model maintenance.

In recent years, there has been a growing number of applications of machine learning techniques to user-adapted interactions. While early work was mainly done in the area of intelligent user interfaces, machine learning methods have also become popular within the user modeling community.

At UM97, a first workshop on "Machine Learning for User Modeling" (ML4UM) took place, and a special interest group was initiated. The second ML4UM workshop was held at the UM99. The ML4UM SIG now has both a web site and a mailing list with about 150 subscribers. The growing interest in machine learning techniques for user modeling and adaptive systems is also reflected by the upcoming special issue on Adaptive User Interfaces of the "Machine Learning" journal (see http://www.isle.org/~aui/mljcfp.html).

The goal of the workshop is twofold: On the one hand, it attempts to be a forum for user modeling researchers who want to discuss specific problems of using machine learning for user modeling. Both experts and novices (and all those in between) are invited. On the other hand, the workshop shall function as a SIG meeting, where joint activities of interested attendants can be planned. Hence, there are two groups of questions to be discussed at the workshop:

1. Research issues:
   • What learning tasks can be identified in user modeling systems?
   • Are there classes of problems in user modeling that are particularly well or poorly suited to the application of machine learning methods?
   • Are there machine learning algorithms or classes of algorithms that are particularly appropriate / not appropriate for user modeling systems?
   • Are there subareas of user modeling or classes of user modeling systems where machine learning can be especially useful?
   • In what respects does the induction of a user model differ from other induction tasks to which machine learning is typically applied, and what implications does this have for the application of machine learning in user modeling?
   • In the case of the description of a concrete application: Why did you choose this particular machine learning technique? How did it affect the success of your application? What general conclusions can you draw from your experiences?
   • Where / How does the user fit into the learning; what kind of user feedback is helpful / needed, and how can the user query / use the learned model?
2. SIG issues:
   • What has been done since the last SIG meeting ?
   • How can SIG facilities be made more useful?
   • What are possibilities for cooperation between SIG members?
   • What could be activities the SIG should engage in?
   • and others

Participation and Paper Submission

• describes why they are interested in the application of machine learning techniques to user modeling and the problems and questions they have encountered and/or
• makes proposals concerning SIG activities. and/or
• describe their current work and interests as related to the workshop topic

Organization

Accepted contributions will be distributed electronically to all participants beforehand. A mailing list will be set up which participants will be encouraged to use for a-priori comments on other participants' contributions.

Submission instructions

There are no further formatting instructions for the first submission. Though, we recommend to use the Springer LLNCS package.

Deadlines

Program Committee

Author: Ralph Schäfer (Ralph.Schaefer@dfki.de)