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Modeling user preference is one of the challenging issues in intelligent information systems. Extensive research has been performed to automatically analyze user preference and to utilize it. One problem still remains: The representation of preference, usually given by measure of vector similarity or probability, does not always correspond to common sense of preference. This problem gets worse in the case of negative preference. To overcome this problem, this paper presents a preference model using mutual information in a statistical framework. This paper also presents a method that combines information of joint features and alleviates problems arising from sparse data. Experimental results, compared with the previous recommendation models, show that the proposed model has the highest accuracy in recommendation tests.
Personalization, user preference, recommendation, user modeling, text mining, mutual information, data sparseness, feature-combining weight, Pareto distribution.

T. Kim, S. Y. Jung and J. Hong, "A Statistical Model for User Preference," in IEEE Transactions on Knowledge & Data Engineering, vol. 17, no. , pp. 834-843, 2005.
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