Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Multilevel hypergraph partitioning: applications in VLSI domain
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
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
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
Leveraging Users for Efficient Interruption Management in Agent-User Systems
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
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The ever-increasing popularity of the Internet has led to an explosive growth of the sheer volume of information. Recommender system is one of the possible solutions to the information overload problem. Traditional item-based collaborative filtering algorithms can provide quick and accurate recommendations by building a model offline. However, they may not be able to provide truly personalized information. For providing efficient and effective recommendations while maintaining a certain degree of personalization, in this paper, we propose a hybrid model-based recommender system which first partitions the user set based on user ratings and then performs item-based collaborative algorithms on the partitions to compute a list of recommendations. We have applied our system to the well known movielens dataset. Three measures (precision, recall and F1-measure) are used to evaluate the performance of the system. The experimental results show that our system is better than traditional collaborative recommender systems.