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 Correlationbased on K-nearest neighborhood. These systems rely on historical rating data and preferences ofusers and items in order to propose appropriate recommendations for active users. These systemsdo not often have a complete matrix of input data. This challenge leads to a decrease in theaccuracy level of recommendations for new users. The exact matrix completion technique tries topredict unknown values in data matrices. This study is to show how the exact matrix completioncan be used as a preprocessing step to tackle the sparseness problem. Compared to application ofthe sparse data matrix, selection of neighborhood set for active user based on the completed datamatrix leads to achieving more similar users. The main advantages of the proposed method arehigher prediction accuracy and an explicit model representation. The experiments show significantimprovement in prediction accuracy in comparison with other substantial methods.