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Department of Information Science and Telecommunications


INFSCI 3957: Adaptive Information Systems

Doctoral Seminar on Systems and Technology: Applications of Information Technology

(Fall 2001, CRN 37345)

[ Formal Data | Course Tools | Syllabus & Schedule | Readings | Course Materials | BlackBoard | InterBook ]

Syllabus and Schedule


Adaptive Information Systems is a relatively new direction of research on the crossroads of Information Science and Human-Computer Interaction, and Artificial Intelligence. One limitation of traditional "static" Information Systems is that they provide the same information and the same search and navigation opportunities to all users. If the user population is relatively diverse, a traditional system will suffer from an inability to be "all things to all people". For example, a traditional educational Information Systems system will present the same static explanation and suggest the same next page to students with widely differing educational goals and knowledge of the subject. Similarly, a static electronic encyclopedia will present the same information and same set of links to related articles to readers with different knowledge and interests. A Web bookstore might also offer the same selection of "bestsellers" to customers with different reading preferences. Finally, a static virtual museum will offer the same "guided tour" and the same narration to visitors with very different goals and background knowledge.

Adaptive Information Systems is an alternative to the traditional "one-size-fits-all" approach in the development of Information Systems. Adaptive Information Systems build a model of the goals, preferences and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt to the needs of that user. For example, a student in an adaptive educational Information Systems system will be given a presentation that is adapted specifically to his or her knowledge of the subject and a suggested set of most relevant links to proceed further. An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests. A virtual museum will adapt the presentation of every visited object to the user's individual path through the museum. These are just a few recent examples of the use of Adaptive Information Systems.


The goal of the seminar is to get immersed into the world of adaptive information systems (AIS). The attendees will learn about many aspects of AIS, get familiar with existing technologies that makes AIS possible, and study many examples of modern AIS. After the end of the seminar the students should be able to develop their own AIS as well as to apply modern adaptation and personalization technologies in the context of larger projects. Those who will successfully complete the seminar will have a chance to join a small cohort of experts on adaptivity and personalization.

The Web personalization industry is on the rise. In the coming 2-3 years universities, research labs, and companies will need hundreds of experts who understand adaptive systems and personalization to lead various exciting projects. This is your chance to be among the leaders of the new and exciting field


After a brief introduction into the field provided by the instructor we will switch to a rather standard work mode. Every week we will read and discuss several articles, book chapters, or practical adaptive systems and tools. Each "lecture" will be devoted to two specific subtopics of the field (see the draft content below) - one particular technology and one particular application area. The attendees will alternate in leading a discussion on one of the technology topics. Leading a discussion means carefully reading the assigned set of papers, preparing a presentation and handling the post-presentation discussion.

The overall goal of the seminar is to draft a detailed syllabus and prepare some course material and reading assignment for a hypothetical MS level course on adaptive information systems. To learn something really well one has to try teaching it. Through our discussion we will try to develop a better structure of the course material. The original set of topics is provided below, but we will have to work together to identify key subtopics and issues within each large topic and to assemble the most relevant readings for each of them.

To complement the "reading part" and to get a real experience in developing adaptive systems the attendees will have to do a small term project. Depending on the level of the programming skills you can either choose to develop a small adaptive information system that uses just 1-2 adaptation technologies, or to develop an adaptive application using one of existing tools such as InterBook (http://www2.sis.pitt.edu/~peterb/InterBook.html ), WBI (http://www.almaden.ibm.com/cs/wbi/ ) or AHA (http://wwwis.win.tue.nl/~debra/ ). (InterBook, for example, will require no programming skills at all).

Course Draft Schedule

We will try to follow the draft schedule below as close as possible. Note that each meeting has a technology part and an application part. Two application area slots are open to accommodate for the application areas that are of special interest to attendees or that will be discovered during the course. The instructor may also change the topics of some meetings to accommodate interests of the attendees (let's be adaptive!).

Wednesday August 29 Introduction
Wednesday September 5 Adaptive navigation support by annotation;
Personalization in Future TV
Wednesday September 12 Adapting to user knowledge;
Adaptive Medical Systems
Wednesday September 19 No class. Knowledge representation panel
Wednesday September 26 Natural language generation for adaptive presentation
Virtual museums, electronic encyclopedias
Wednesday October 3 Adapting to platform and context (time, place, bandwidth)
Mobile tourist and museum guides
Wednesday October 10 Agent-based architectures for adaptive systems
Intelligent Tutoring Systems
Wednesday October 17 User modeling with stereotypes
Discussion of course projects
Wednesday October 24 No class. Work on projects
Wednesday October 31 Tools for developing personalized systems
Adaptive systems for E-commerce
Wednesday November 7 Collaborative filtering and adaptive recommendation, user interests
Adaptive information systems and kiosks
Wednesday November 14 Neural networks and machine learning in adaptive systems
Adaptive IR systems and services
Wednesday November 21 No class. Thanksgiving Recess
Wednesday November 28 Adaptive Educational Systems and Student Modeling
Practical Web-based education
Wednesday December 5 Adapting to cognitive traits and users with special needs
Adaptive Help and Performance Support Systems
Wednesday December 12 Legal issues (pending)
Project presentations

Assignments and Grading

There are four things each regular student is expected to do:

  1. Each student contribute relevant resources to the pool of resources (relevant papers, known adaptive systems on the Web, known tools, etc). To avoid intersections each student will be assigned a database of literature to search in. However, everyone can contribute relevant resources found anywhere.
  2. Every week each student reads a (small) paper on the subject of the coming lecture, prepares a 1 page summary and delivers a small 7-10 minutes presentation. An RTF file with summary and a PowerPoint file with slides have to be sent to the instructor on the day of class. The files will be placed on the course Web site. Printed copies of the summary have to be brought to class to distribute among the attendees.
  3. Once or twice during the seminar duration each student serves as discussion leader for the meeting. That means reading 4-5 papers on the technology subject (preferably some review papers), preparing a presentation for 30-40 minutes, chairing the session and leading the discussion. Allow at least 2 weeks to prepare your original presentation. After the meeting the leader has to prepare 3-5 page summary of the topics of the meeting that includes his own findings and results of the discussion.
  4. Each student has to do a term project - implementing a small adaptive system using a programming language (for IS students) or some development tool (for non-programmers).

The final grade will be assembled from your performance in:

It is assumed that every student will serve as a discussion leader 1-2 times and will prepare at least 10 paper summaries.

Classification of adaptive systems:

Lecture by lecture we will consider several aspects of adaptive systems from application areas to technologies to implementation. On most of the lectures we will be also analyzing several examples of adaptive systems trying to "dissect" and classify them from several aspects. The list of aspects with some coarse grain classification is provided below. This list will serve as the draft structure of our work. However, we expect to enhance this classification (including adding new aspects) during the seminar.

Application areas:

Adaptation technologies:

AI technologies:

User modeling technologies:

Context and platform:


Legal aspects of adaptive systems:

Course Policies

Academic Integrity

You are expected to be fully aware of your responsibility to maintain a high quality of integrity in all of your work. All work must be your own, unless collaboration is specifically and explicitly permitted as in the course group project. Any unauthorized collaboration or copying will at minimum result in no credit for the affected assignment and may be subject to further action under the University Guidelines for Academic Integrity. You are expected to have read and understood these Guidelines. A document discussing these guidelines was included in your orientation materials.

Special Considerations

If you have a disability that requires special testing accommodations or other classroom modifications, please, notify both the instructor and Disability Resources and Services by the second week of the term. You may be asked to provide documentation of your disability to determine the appropriateness of accommodations. To notify Disability Resources and Services, call 64807890 (voice or TDD) to schedule and appointment. The office is located in the William Pitt Union, Room 216,


This is a list of major sources that we will use through the course. More detailed reading lists will be assembled for each lecture.

Bauer, M., Gmytrasiawicz, P., and Vassileva, J. (eds.) (2001) User Modeling 2001. Lecture Notes in Artificial Intelligence, Vol. 2109, Berlin: Springer-Verlag.

Brusilovsky, P., Kobsa, A., and Vassileva, J. (eds.) (1998) Adaptive Hypertext and Hypermedia. Dordrecht: Kluwer Academic Publishers.

Brusilovsky, P., Stock, O., and Strapparava, C. (eds.) (2000) Adaptive Hypermedia and Adaptive Web-based Systems, AH2000. Lecture Notes in Computer Science, Vol. 1892, Berlin: Springer-Verlag.

Moore, J. D., Redfield, C. L., and Johnson, W. L. (eds.) (2001) Artificial Intelligence in Education. Amsterdam: IOS Press.

Schneider-Hufschmidt, M., Kühme, T., and Malinowski, U. (eds.) (1993) Adaptive user interfaces: Principles and practice. Human Factors in Information Technology, Amsterdam: North-Holland.

Copyright © 2001 Peter Brusilovsky