Agents that reduce work and information overload
Communications of the ACM
Generating queries and replies during information-seeking interactions
International Journal of Human-Computer Studies
A Feature-based Approach to Recommending Selections based on Past Preferences
User Modeling and User-Adapted Interaction
Making Rational Decisions Using Adaptive Utility Elicitation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A framework for the interactive customization of products and services
A framework for the interactive customization of products and services
Resolving plan ambiguity for cooperative response generation
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Adaptive provision of evaluation-oriented information: tasks and techniques
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Knowledge-based navigation of complex information spaces
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
IEEE Transactions on Knowledge and Data Engineering
Reasoning about interaction in a multi-user system
UM'05 Proceedings of the 10th international conference on User Modeling
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Mass customization requires acquisition of customer preferences, which can be modeled with multi-attribute utility theory (MAUT). Unfortunately current methods of acquiring MAUT weights and utility functions require too many queries. In Iona, the user is first queried for absolute/preferred constraints and categorical preferences to cull the product pool. Next Iona selects queries to maximally reduce the utility uncertainty of the remaining product choices. Implemented queries include stereotype membership and contexts (the purchase situation), which give probabilistic MAUT data modeled as ranges of weights. The usefulness of a query is based on the reduction in uncertainty (smaller range) weighted by the likelihood that the user belongs to a stereotype/context based on similarity to the current user model. Querying proceeds until the usefulness of the best query is below the threshold of user impatience. Finally integer programming is used to select the best product for the user.