MLIRUM'03: The Second Workshop on Machine Learning, Information Retrieval and User Modeling http://www.cs.rutgers.edu/mlirum/mlirum-2003 at the Ninth International Conference on User Modeling http://www2.sis.pitt.edu/~um2003/index.html June 22-26, Pittsburgh, PA, USA CALL FOR PAPERS ------------------------------------------------------------------------ HISTORY 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 and the Third at the UM2001. The ML4UM SIG now has both a web site and a mailing list. At UM2001, the first workshop on "Machine Learning, Information Retrieval and User Modeling" (ML,IR, and UM) took place. We have merged these two workshops and SIGs, as they have such related topics. ------------------------------------------------------------------------ BACKGROUND AND MOTIVATION User model acquisition is a difficult problem. In Machine Learning, 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. In Information Retrieval, user models have been limited to lists of terms relevant to an information need. The list is usually very short for ad hoc querying and longer for information filtering tasks. Information systems that could benefit from having a user model should be able to adapt to individual users, to learn about their preferences and attitudes during the interaction (to construct a user profile), and memorize them for later use. Moreover, these user profiles could represent a starting point for the creation of user communities based on shared interests or goals. Further, the system should be able to update its model is a user changes interests. Machine Learning (ML) is concerned with the formation of models from observations. Hence, learning algorithms seem to be promising candidates for user model acquisition systems. Information Retrieval (IR) is concerned with the study of systems for representing, organising, retrieving and delivering information based on content. User modeling is the glue. As the better we model users, the better we can satisfy their information needs. We also aim to provide a forum for researchers who are not necessarily familiar with the diverse aspects of UM/ML/IR to be able to get acquainted with the possibilities of collaboration between the communities. Thus, our main goal is to build further bridges between three communities: User Modeling, Machine Learning, and Information Retrieval. We welcome your contributions to addressing these issues. Our main goal is to build further bridges between three communities: User Modeling, Machine Learning, and Information Retrieval. ------------------------------------------------------------------------ QUESTIONS TO BE ADDRESSED: The two primary questions we would like to address are: 1. How can we apply Machine Learning and Information Retrieval techniques to acquire and continuously adapt user models? * What role can and should the user play in reviewing and refining their own model? * What are issues in modeling the user vs. modeling the intermediary for IR? * How can intelligent agents be used when in charge of managing the interaction with an information system? * How can we evaluate user-adaptive IR systems? Is it based on effective retrieval, user experience, reaction and satisfaction? * Where/How does the user fit into the picture? What kind of user feedback is helpful/needed, and how can the user query/use the learned model? * How can ML be used for building user communities based on common interests, and background? How do you apply IR techniques to these? * In the case of the description of a concrete application: Why did you choose these particular techniques? How did they affect the success of your application? What general conclusions can you draw from your experiences? 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? * How can we get more people involved? * What are the issues/problems that drive current research? * What are the ways we can combine these three fields such that changes in any field does not break the integrated system? Are there any standards or good practices for integration that can be identified to address this issue at this stage? ------------------------------------------------------------------------ WORKSHOP FORMAT The workshop program will be content-centered. Papers on related topics will be grouped together into sessions, each of which will be presented by a participant. Each session will have a small discussion at the end to discuss issues related to that topic. General research issues will be separated from SIG issues, which will be discussed at the end of the workshop. ------------------------------------------------------------------------ SUBMISSIONS Authors are required to submit papers not exceeding 10 pages as a PS or PDF file. Each submission is required to address at least one of the main workshop questions. Fulfillment of this requirement will be assessed in the course of the review process. Workshop papers will be published in full length in the workshop proceedings and presented in talks at the workshop. Submissions should be made to Ayse Goker . Authors are also requested to send and email to Ayse Goker containing the title of the paper, the name of the file that has been submitted, the author name(s), the author affiliation(s) and contact information. Any queries regarding submission should be sent to: Ayse Goker, (asga@scms.rgu.ac.uk) or Sofus A. Macskassy, (smacskas@stern.nyu.edu) ------------------------------------------------------------------------ IMPORTANT DATES March 1: Submission deadline for Workshop papers March 24: Notification of Workshop authors April 3: Early Registration Deadline for the conference April 15: Camera ready copies due ------------------------------------------------------------------------ ORGANIZERS * Sofus A. Macskassy (smacskas@stern.nyu.edu) Leonard N. Stern School of Business, NYU * Ross Wilkinson (ross.wilkinson@csiro.au) CSIRO * Ayse Goker (asga@scms.rgu.ac.uk) Robert Gordon University * Mathias Bauer (bauer@dfki.de) DFKI