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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.