About Us
This website is meant as a portal for researchers in the areas of
Machine Learning, Information Retrieval and User Modeling. In
particular, researchers who apply techniques from all three fields
in order to improve their performance or get more rich models.
Maintainers of this website:
- Sofus A. Macskassy: Currently the primary caretaker of the website.
In charge of content, design, layout, and running of the website.
Co-organizer of the Third ML4UM Workshop
(see Archives).
- Martin E. Müller: Helps with providing content for various pages.
Hopefully, he'll help maintain the site as things get set up. Martin
was a co-organizer of the Third ML4UM Workshop (see Archives).
- Ralph Schäfer: Helps with providing content for various pages.
Hopefully, he'll help maintain the site as things get set up. Ralph
was primary organizer of the Third ML4UM Workshop (see Archives). Ralph is currently also in charge of getting the
ml4um mailing list back on its feet.
- Ayse Goker-Arslan: Helps with providing content for various pages.
Hopefully, she'll help maintain the site as things get set up. Ayse
was primary organizer of the First ML, IR and UM Workshop (see Archives).
- Ross Wilkinson: Helps with providing content for various pages.
Hopefully, he'll help maintain the site as things get set up. Ross
was co-organizer of the First ML, IR and UM Workshop (see Archives).
- If you would like to help maintain the web-site,
let us know!
Goals of this website
Questions and issues we are posing and trying to answer include:
- 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?
- Moving user models beyond queries in IR;
- Modeling the user vs. modeling the intermediary for IR;
- Matching algorithms when user models are more sophisticated;
- Exploring information delivery models when user models are more
sophisticated (using both better matching and adaptive delivery);
- Acquisition of user models appropriate to an information environment;
- ML solutions to support to the navigation of Web sites;
- ML solutions for intelligent information retrieval, especially in large
repositories, e.g. Digital Libraries;
- ML for extraction and management of user profiles;
- ML for building user communities based on common interests, and background;
- Intelligent agents in charge of managing the interaction;
- User interaction in intelligent IR;
- Evaluation of user-adaptive IR systems;
- Intelligent user interfaces in IR;
- Personalization of Web sites;
- Personalization for Web users;
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