========================================================================= Call for participation Machine Learning for User Modeling UM-2001 Workshop (http://www.dfki.de/um2001/) ========================================================================= User model acquisition is a difficult problem. The information available to a user modeling system is usually limited, and it is hard to infer assumptions about the user that are strong enough to justify non-trivial conclusions. Classical acquisition methods like user interviews, application-specific heuristics, and stereotypical inferences often are inflexible and unsatisfying. 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: 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? 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? - others Participation and Paper Submission ================================== Participants are required to submit a short paper that - 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 In the first two cases, authors shall provide comments and answers to the questions above as topics of interest, and perhaps raise new relevant questions and issues in about 2 pages. In the third case, the work and interests should be described in no more than 10 pages. Participants will be selected based on their submissions. Organization ============ The workshop program will be content-centered. Related issues will be grouped together into sessions, each of which will be moderated by one other participant. Participants will be given opportunity to briefly present their contributions, but they may be part of several sessions, if their paper covers several issues that are quite different from each other. In particular, research issues will be separated from SIG issues. 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 ======================= Please submit a short paper in PostScript, PDF, or HTML to Ralph.Schaefer@dfki.de. The final version should not exceed 10 pages. There are no further formatting instructions for the first submission. Though, we recommend to use the Springer LLNCS package. Deadlines ========= March 8 deadline for submissions April 1 notification of authors about acceptance April 27 deadline for revised versions of accepted contributions May 11 accepted contributions and first draft of the workshop program made available to participants; mailing list for participants set up Program Committee ================= Ralph Schaefer, DFKI ,Germany, Ralph.Schaefer@dfki.de (Organizer) Martin E. Mueller, University of Osnabrueck, Germany, Martin.E.Mueller@uos.de (Organizer) Sofus Attila Macskassy, Rutgers University, U.S.A., sofmac@cs.rutgers.edu (Organizer) Mathias Bauer, DFKI, Germany, bauer@dfki.de Piotr Gmytrasiewicz, Univ. of Texas at Arlington, U.S.A., piotr@huckle.uta.edu Mehmet Goeker, DaimlerChrysler Research and Technology, Palo Alto, U.S.A., mehmet.goeker@daimlerchrysler.com Ingo Schwab, GMD, St. Augustin, Germany, ingo.schwab@gmd.de Jude Shavlik, University of Wisconsin, Madison, U.S.A., shavlik@cs.wisc.edu Frank Wittig, University of Saarbruecken, Germany, wittig@cs.uni-sb.de