A Neighbor Selection Method Based on Priority of the Number of Common Items in Collaborative Filtering

Memory-based collaborative filtering (MBCF) selects the top-k neighbors in order to predict a rating for an item the target user has not yet experienced. We propose a method to minimize the similarity errors with the existing neighbor selection method by considering the number of common items between two objects. To verify the proposed method, we compare… CONTINUE READING