Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Statistical Model for User Preference
IEEE Transactions on Knowledge and Data Engineering
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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User preference modeling is one of the challenging issues in intelligent information system. Extensive researches have been performed to automatically analyze user preference and utilize it. But one problem still remains: All of them could not deal with semantic preference representation and uncertain data at the same time. To overcome this problem, this paper proposes a rough set approach to user preference modeling. A family of atomic preference granules supporting semantic in knowledge space and two operators, called vertical aggregation operator ⊙ and horizon combination operator ⊕ respectively, are defined to represent user preference. Based on this, a rough granular computing framework is also constructed for user preference analyzing. Experimental results show that the proposed model plays well in recommendation tests.