A kind of Recommendation Algorithm of Matrix Decomposition Based on Power Grid User Rating Characteristics

Abstract

Recommender system can effectively solve the information overload problem in the era of big data. Recent research on recommender systems, specifically Collaborative Filtering, has focused on Matrix Factorization methods, which have been shown to have excellent performance in the recommendation. However, these methods do not pay attention to the influence of user's rating characteristics, which are especially important for the accuracy of prediction or recommendation. Therefore, in order to get better performance, this paper proposes a novel method based on matrix factorization. We consider that the user's rating score is composed of two parts, one part is called real score which is decided by the user's preferences, and another part is called bias score, which is decided by the user's rating characteristics. We analyze the user's historical behavior to find his rating characteristics by using the matrix factorization technique, and then use them to adjust the final prediction results. By comparing with the latest algorithms on the existing datasets, we verified that the proposed method can significantly improve the accuracy of recommender systems and achieves the best performance in terms of prediction accuracy criterion over the state-of-the-art methods.