Helgon: extending the retrieval by reformulation paradigm
CHI '89 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision-theoretic troubleshooting
Communications of the ACM
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
A New Criterion in Selection and Discretization of Attributes for the Generation of Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
An adaptive interactive agent for route advice
Proceedings of the third annual conference on Autonomous Agents
Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Tailoring the interaction with users in electronic shops
UM '99 Proceedings of the seventh international conference on User modeling
User Modeling and User-Adapted Interaction
Machine Learning
Adaptive provision of evaluation-oriented information: tasks and techniques
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Methodological Considerations on Chance Discovery
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Users Modeling for Adaptive Call Centers
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
An adaptive videos enrichment system based on decision trees for people with sensory disabilities
Proceedings of the International Cross-Disciplinary Conference on Web Accessibility
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An emerging practice in e-commerce systems is to conduct interviews with buyers in order to identify their needs. The goal of such an interview is to determine sets of items that match implicit requirements. Decision trees structure the interview process by defining which question follows a given answer. One problem related to decision trees is that changes in the selling strategy or product mix require complex tree restructuring efforts. In this paper we present a framework that represents the selling strategy as a set of parameters, reflecting the preferences of sellers and buyers. This representation of the strategy can be used to generate optimized decision trees in an iterative process, which exploits information about historical buyer behavior. Furthermore, the framework also supports advanced optimization strategies such as dynamic parameter adaptation and exit risk minimization.