GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Collaborative filtering with decoupled models for preferences and ratings
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Recency-based collaborative filtering
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
Improving Prediction Quality in Collaborative Filtering Based on Clustering
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Alternative Formulas for Rating Prediction Using Collaborative Filtering
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Application of color change feature in gastroscopic image retrieval
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Integrating user feedback with heuristic security and privacy management systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Enhanced information retrieval using domain-specific recommender models
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Quality and Leniency in Online Collaborative Rating Systems
ACM Transactions on the Web (TWEB)
IRFCF: iterative rating filling collaborative filtering algorithm
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
Insights from enterprise assessment: How to analyze LESAT results for enterprise transformation
Information-Knowledge-Systems Management
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The goal of collaborative filtering is to make recommendations for a test user by utilizing the rating information of users who share interests similar to the test user. Because ratings are determined not only by user interests but also the rating habits of users, it is important to normalize ratings of different users to the same scale. In this paper, we compare two different normalization strategies for user ratings, namely the Gaussian normalization method and the decoupling normalization method. Particularly, we incorporated these two rating normalization methods into two collaborative filtering algorithms, and evaluated their effectiveness on the EachMovie dataset. The experiment results have shown that the decoupling method for rating normalization is more effective than the Gaussian normalization method in improving the performance of collaborative filtering algorithms.