GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Meta-recommendation systems: user-controlled integration of diverse recommendations
Proceedings of the eleventh international conference on Information and knowledge management
An Adaptive Recommendation System without Explicit Acquisition of User Relevance Feedback
Distributed and Parallel Databases
A Hybrid Movie Recommender System Based on Neural Networks
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
ACM Transactions on Information Systems (TOIS)
A Similarity Measure for Collaborative Filtering with Implicit Feedback
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of 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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In this paper, we propose a new method for collaborative filtering (CF)-based recommender systems. Traditional CF-based recommendation algorithms have applied constant settings such as a reference group (neighborhood) size and a significance level to all users. In this paper we develop a new method that identifies optimal personalized settings for each user and applies them to generating recommendations for individual users. Personalized parameters are identified through iterative simulations with 'training' and 'verification' datasets. The method is compared with traditional 'constant settings' methods using Netflix data. The results show that the new method outperforms traditional, ordinary CF. Implications and future research directions are also discussed.