Mixed linear matrix completion model based on auxiliary information

The matrix completion technology has been applied in many fields in recent years. A matrix completion model that mixes bilinear and unilateral linear relationship is proposed, considering the correlation between row information and column information and their respective characteristics, so that the mixed linear model can approximate the original matrix entries. The convergence of using the ADMM algorithm to solve the convex optimization problem is proved, and makes two sets of experiments with synthetic datasets and real datasets, which proves that the proposed method is more effective compared with the existing model using auxiliary information, whose error under RMSE evaluation standard has been reduced by more than 25%.