A collaborative filtering recommendation model using polynomial regression approach

In gird environment, collaborative filtering (CF) could be used for security recommendation when grid users face plenty of unknown security grid services. Also, CF recommender systems could be employed in the virtual machines managing platform to measure the creditability of each virtual machine. In this study, a polynomial regression based recommendation model on the basis of typical user-based CF is built to make security recommendation. In the model, a cluster of recommendation algorithms based on polynomial regression are derived according to various regression orders and dataset sizes. From our experiments, three significant conclusions are discovered in this model. Firstly, algorithms with lower regression orders make better predictions. Secondly, among algorithms with each fixed regression order, the best one satisfies that its dataset size is equal to its regression order in general. Thirdly, selecting appropriate regression order and dataset size could enhance recommendation quality.