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.
In a subsequent study, Jin and Si REF compare Gaussian and decoupling normalization approaches within CF domain and their experimental results demonstrate that decoupling normalization is more effective than Gaussian normalization method in terms of prediction accuracy.