Adaptive User Interfaces and Personalization
In the mid-1990's, I became aware that, in many domains, advisory
systems were proving more successful that expert systems, since the
domain users wanted support rather than replacement. Clearly, one
could use machine learning to acquire the knowledge for such advisory
systems, which had led to earlier interest in "learning apprentices".
However, the increasing reliance on graphical user interfaces gave
the new advisors a different character from the older systems, supporting
new methods for collecting trace data about user behavior and new ways
to provide advice. My interest in this topic, and the approach that
my colleagues and I have taken, were influenced strongly by Jeff
Schlimmer's work on what he called self-customizing software.
However, I prefer to call them adaptive user interfaces.
In 1996, I joined the DaimlerChrysler Research and Technology Center,
where I started three projects along these lines. One involved the
Adaptive Route Advisor,
which offered a personalized service for in-car navigation based on
profiles learned from user decisions. Another system, the
Adaptive Place Advisor,
carried out a spoken-language dialogue to help users decide on a
suitable destinations, such as restaurants, and utilized these
conversations to update its user profile. We also collaborated with
Daniel Billsus and Michael Pazzani at UCI on their News Dude system,
which learned, from implicit feedback, which news stories to read to
Another system, INCA,
developed in parallel at ISLE, applied similar ideas to an interactive
system for crisis planning and scheduling, with user profiles again being
based on past traces of user behavior.
Since then, we have utilized this approach to unobtrusive personalization
to develop a number of other prototype systems that automatically
learning about users from interacting with them. These include an
adaptive music player, a personalized travel agent, an adaptive
bookmarking system, an online apartment finder, and, most recently,
a personalized stock tracker that helps users detect stocks they
may want to trade. We have also developed a flexible software
environment that makes the construction of such adaptive users
interfaces efficient and effective.
Thompson, C. A., Göker, M., & Langley, P. (2004).
A personalized system for conversational recommendations.
Journal of Artificial Intelligence Research, 21, 393-428.
Yoo, J., Gervasio, M., & Langley, P. (2003).
An adaptive stock tracker for personalized trading advice.
Proceedings of the International Conference on Intelligent User
Interfaces (pp. 197-203). Miami, Florida.
Fiechter, C.-N., & Rogers, S. (2000).
Learning subjective functions with wide margins.
Proceedings of the Seventeenth International Conference on Machine
Learning (pp. 287-294). Stanford, CA.
Rogers, S., Fiechter, C.-N., & Thompson, C. (2000).
Adaptive user interfaces for automotive environments.
Proceedings of the IEEE
Intelligent Vehicles Symposium. Dearborn, MI.
Langley, P., Thompson, C., Elio, R. & Haddadi, A. (1999).
An adaptive conversational interface for destination advice.
Proceedings of the Third International Workshop on Cooperative
Information Agents (pp. 347-364). Uppsala, Sweden.
Gervasio, M. T., Iba, W., & Langley, P. (1999).
Learning user evaluation functions for adaptive scheduling assistance.
Proceedings of the Sixteenth International Conference on Machine
Learning (pp. 152-161). Bled, Slovenia: Morgan Kaufmann.
Langley, P. (1999).
User modeling in adaptive interfaces.
Proceedings of the Seventh International Conference on User
Modeling (pp. 357-370). Banff, Alberta: Springer.
Rogers, S., Fiechter, C., & Langley, P. (1999).
An adaptive interactive agent for route advice.
Proceedings of the Third International Conference on Autonomous
Agents (pp. 198-205). Seattle: ACM Press.
Rogers, S., Fiechter, C., & Langley, P. (1999).
A route advice agent that models driver preferences.
Proceedings of the AAAI Spring Symposium on Agents with Adjustable
Autonomy. Stanford, CA: AAAI Press.
Langley, P., & Fehling, M. (1998).
The experimental study of adaptive user interfaces (Technical Report
98-3). Institute for the Study of Learning and Expertise, Palo Alto, CA.
Iba, W., Gervasio, M., Langley, P., & Sage, S. (1998).
Evaluating computational assistance for crisis response.
Proceedings of the Twentieth Annual Conference of the Cognitive
Science Society. Madison, WI: Lawrence Erlbaum.
Gervasio, M., Iba, W., & Langley, P. (2008).
Case-based seeding for an interactive crisis response assistant.
Proceedings of the AAAI-98 Workshop on Case-Based Reasoning Integrations.
Gervasio, M., Iba, W., & Langley, P. (1998).
Learning to predict user operations for adaptive scheduling.
Proceedings of the Fifteenth National Conference on Artificial
Intelligence (pp. 721-726). Madison, WI: AAAI Press.
Rogers, S., & Langley, P. (1998). Interactive refinement of route
preferences for driving. Proceedings of the AAAI Spring Symposium
on Interactive and Mixed-Initiative Decision-Theoretic Systems.
Stanford, CA: AAAI Press.
Gervasio, M., Iba, W., Langley, P., & Sage, S. (1998).
Interactive adaptation for crisis response.
Proceedings of the AIPS-98 Workshop on Interactive and Collaborative
Planning (pp. 29-36). Pittsburgh, PA.
Rogers, S., Langley, P., Johnson, B., & Liu, A. (1997).
Personalization of the automotive information environment.
Proceedings of the Workshop on Machine Learning in the Real World:
Methodological Aspects and Implications. Nashville, TN.
Langley, P. (1997).
Machine learning for adaptive user interfaces.
Proceedings of the 21st German Annual Conference on Artificial
Intelligence (pp. 53-62). Freiburg, Germany: Springer.
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