Corpus ID: 2119324

Learning User Preference Models under Uncertainty for Personalized Recommendation

@inproceedings{Zenebe2006LearningUP,
  title={Learning User Preference Models under Uncertainty for Personalized Recommendation},
  author={A. Zenebe and L. Zhou and A. F. Norcio},
  year={2006}
}
Preference modeling has a crucial role in customer relationship management systems. Traditional approaches to preference modeling are based on decision and utility theory by explicitly querying users about the behavior of value function, or utility of every outcome with regard to each decision criterion. They are error-prone and labor intensive. To address these limitations, computer based implicit elicitation approaches have been proposed. However, the extant approaches to implicit elicitation… Expand

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