Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
RecTree: An Efficient Collaborative Filtering Method
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
An MDP-Based Recommender System
The Journal of Machine Learning Research
Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first propose a new CF model-based approach which has been implemented by basing on mining frequent itemsets technique with the assumption that "The larger the support of an item is, the higher it's likely that this item will occur in some frequent itemset, is". We then present the enhanced techniques such as the followings: bits representations, bits matching as well bits mining in order to speeding-up the algorithm processing with CF method.