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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A system for automatic personalized tracking of scientific literature on the Web
Proceedings of the fourth ACM conference on Digital libraries
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Machine Learning
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Explaining away ambiguity: learning verb selectional preference with Bayesian networks
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
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Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
International Journal of Electronic Commerce
ODAM: An Optimized Distributed Association Rule Mining Algorithm
IEEE Distributed Systems Online
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This paper proposes a novel approach for estimating the statistical multimedia user preference by providing weights to multimedia contents with respective to their consumed time. The optimal weights can be obtained by training the statistical system in the sense that the mutual information between old preference and current preference is maximized. The weighting scheme can be done by partitioning a user's consumption history data into smaller sets in a time axis. With developing a mathematical derivation of our learning method, experiments were implemented for predicting the TV genre preference using 2,000 TV viewers' watching history and showed that the performance of our method is better than that of the typical method.