Abstract
In order to customize the display of meteorological data for different users, we use clustering to group similar users. We compute a rate of similarity between the current user and all others in the same cluster. We use this rate for weighting users’ preferences and then compute an average to be compared with a threshold to decide to display this parameter or not. The optimization of this threshold is also discussed.
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Mouine, M., Lapalme, G. (2013). Preference Thresholds Optimization by Interactive Variation. In: Zaïane, O.R., Zilles, S. (eds) Advances in Artificial Intelligence. Canadian AI 2013. Lecture Notes in Computer Science(), vol 7884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38457-8_28
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DOI: https://doi.org/10.1007/978-3-642-38457-8_28
Publisher Name: Springer, Berlin, Heidelberg
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