Imputing missing values in Collaborative Filtering using pattern frequent itemsets

Lately, recommendation system has an important role in providing advice on products and services to match the various requirements of users. The popular method for developing recommender system is Collaborative Filtering. This method will search for other users in the systems that are interested by the same or similar items. With this method, users need not to know each other. The system will then suggest choices of other users that might be interested by the current user. However this technique is not work well with scarce data. This problem is known as the sparsity problem. Therefore, we propose to modify Collaborative Filtering using frequent itemsets by imputing the missing value. According to experimental results, the proposed method can properly fill up the missing values and improve the accuracy of recommendations to users with MAE of 0.55 with the neighborhood size of 30.