Title :
CPrefMiner: An Algorithm for Mining User Contextual Preferences Based on Bayesian Networks
Author :
de Amo, Sandra ; Bueno, M.L.P. ; Alves, Gabriel ; Silva, N.F.
Author_Institution :
Sch. of Comput. Sci., Fed. Univ. of Uberlandia, Uberlandia, Brazil
Abstract :
In this article we propose CPrefMiner, a mining technique for learning a Bayesian Preference Network (BPN) from a given sample of user choices. In our approach, user preferences are not static and may vary according to a multitude of user contexts. So, we name them Contextual Preferences. Contextual Preferences can be naturally expressed by a BPN. The method has been evaluated in a series of experiments executed on synthetic and real-world datasets and proved to be efficient to discover user contextual preferences.
Keywords :
behavioural sciences computing; belief networks; data mining; learning (artificial intelligence); BPN learning; Bayesian networks; Bayesian preference network learning; CPrefMiner mining technique; nonstatic user preferences; real-world datasets; synthetic datasets; user choices; user context multitude; user contextual preference mining; Bayes methods; Context modeling; Databases; Genetic algorithms; Motion pictures; Sociology; Statistics; bayesian networks; data mining; genetic programming; preference learning;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on
Conference_Location :
Athens
Print_ISBN :
978-1-4799-0227-9
DOI :
10.1109/ICTAI.2012.24
