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The problem of learning Conditional Preference Networks (CP-nets) from a set of pairwise comparisons between outcomes has received great attention recently. However, because of the randomicity of the users’ behaviors or the observation errors, there exists some noise (errors) in the training samples. Most existing methods neglect to handle the case with noisy samples. In this work, we introduce a...
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