Abstract
Recent research work on preference mining has focused on the development of methods for mining a preference model from preference data following a crisp pairwise representation. In this representation, the user has two options regarding a pair of objects u and v: either he/she prefers u to v or v to u. In this article, we propose FuzzyPrefMiner, a method for extracting fuzzy contextual preference models from fuzzy preference data characterized by the fact that, given two objects u, v the user has a spectrum of options according to his degree of preference on u and v. Accordingly, the mined preference model is fuzzy, in the sense that it is capable to predict, given two new objects u and v, the degree of preference the user would assign to these objects. The efficiency of FuzzyPrefMiner is analysed through a series of experiments on real datasets.
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de Amo, S., Ramos Costa, J.A. (2014). Mining Fuzzy Contextual Preferences. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2014. Lecture Notes in Computer Science, vol 8646. Springer, Cham. https://doi.org/10.1007/978-3-319-10160-6_10
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DOI: https://doi.org/10.1007/978-3-319-10160-6_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10159-0
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