An Item-based collaborative filtering method using Item-based hybrid similarity

Item-based collaborative filtering is a preferred technique on recommender system. It uses a value of item rating similarity to predict user´s preference. In this paper, we include values of item attribute similarity to adjust the predicted rating equation for target item. The results of Item-based collaborative filtering that hybrid item rating similarity and item attribute similarity techniques have Mean Absolute Error (MAE) less than a traditional Item-based collaborative filtering technique and others. The proposed algorithm is efficient to predict better than traditional algorithm as shown in our experiments.