Representation and learning in information retrieval
Representation and learning in information retrieval
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multilevel k-way hypergraph partitioning
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Context Model Based CF Using HMM for Improved Recommendation
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
Hybrid music filtering for recommendation based ubiquitous computing environment
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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The user predicting preference method using a collaborative filtering (CF) does not only reflect any contents about items but also solve the sparsity and first-rater problem. In this paper, we suggest the method of prediction by using associative user clustering and Bayesian estimated value to complement the problems of the current collaborative filtering system. The Representative Attribute-Neighborhood is for an active user to select the nearest neighbors who have similar preference through extracting the representative attributes that most affects the preference. Associative user behavior pattern 3_UB(associative users are composed of 3-users) is clustered according to the genre through Association Rule Hypergraph Partitioning Algorithm, and new users are classified into one of these genres by Naive Bayes classifier. Besides, to get the similarity between users belonged to the classified genre and new users, this paper allows the different estimated values to items which users evaluated through Naive Bayes learning. We evaluate our method on a large CF database of user rating and it significantly outperforms the previous proposed method.