A NOVEL COLLABORATIVE FILTERING MODEL BASED ON COMBINATION OF CORRELATION METHOD WITH MATRIX COMPLETION TECHNIQUE

One of the fundamental methods used in collaborative filtering systems is Correlation
based on K-nearest neighborhood. These systems rely on historical rating data and preferences of
users and items in order to propose appropriate recommendations for active users. These systems
do not often have a complete matrix of input data. This challenge leads to a decrease in the
accuracy level of recommendations for new users. The exact matrix completion technique tries to
predict unknown values in data matrices. This study is to show how the exact matrix completion
can be used as a preprocessing step to tackle the sparseness problem. Compared to application of
the sparse data matrix, selection of neighborhood set for active user based on the completed data
matrix leads to achieving more similar users. The main advantages of the proposed method are
higher prediction accuracy and an explicit model representation. The experiments show significant
improvement in prediction accuracy in comparison with other substantial methods.