An effective collaborative filtering algorithm based on adjusted user-item rating matrix

User-item rating data preprocessing is an important factor that influences the accuracy of the collaborative filtering algorithms. When users assign a rating to an item, the rating may be influenced by some external factors, such as users' emotional factor. By analyzing the deviation of the users' ratings, this paper presents a novel recommendation method based on adjusted user-item rating matrix. In this method, we calculate the users' ratings deviation of each item. Then, according the numbers of users who have ratings on the same item, the item ratings are weighted calculated. Finally, every rating will be adjusted and a normalization user-item rating matrix can be obtained. Based on the adjusted user-item rating matrix, we calculate the similarity between users to produce a rating prediction. The experimental results MovieLen(100K) dataset show that, compared with the traditional collaborative filtering algorithm, the proposed method can effectively improve the accuracy of the recommender system.