Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
Agents that reduce work and information overload
Communications of the ACM
Compositional instance-based learning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Learning to Assess from Pair-Wise Comparisons
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Acquiring Customer Preferences from Return-Set Selections
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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Knowledge of the preferences of individual users is essential for intelligent systems whose performance is tailored for individual users, such as agents that interact with human users, instructional environments, and learning apprentice systems. Various memory-based, instance-based, and case-based systems have been developed for preference modeling, but these system have generally not addressed the task of selecting examples to use as queries to the user. This paper describes UGAMA, an approach to learning preference criteria through active exploration. Under this approach, Unit Gradient Approximations (UGAs) of the underlying quality function are obtained at a set of reference points through a series of queries to the user. Equivalence sets of UGAs are then merged and aligned (MA) with the apparent boundaries between linear regions. In an empirical evaluation with artificial data, use of UGAs as training data for an instance-based ranking algorithm (1ARC) led to more accurate ranking than training with random instances, and use of UGAMA led to greater ranking accuracy than UGAs alone.