In this paper, we propose a novel hybrid method that combines user–user CF with the attributes of DF to indicate the nearest users, and compare four classiers against each other. This method has been developed through an investigation of ways to reduce the errors in rating predictions based on users' past interactions, which leads to improved prediction accuracy in all four classi¯cation algorithms. |