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

Volume

1

fYear

2012

fDate

7-9 Nov. 2012

Firstpage

114

Lastpage

121

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on

Conference_Location

Athens

ISSN

1082-3409

Print_ISBN

978-1-4799-0227-9

Type

conf

DOI

10.1109/ICTAI.2012.24

Filename

6495036