Abstract:

To improve the accuracy of the recommendation algorithm under privacy protection, a logic regression matrix factorization recommendation algorithm is proposed for differential privacy protection. The algorithm first converts the matrix decomposition of implicit data into a classification problem to model it in a probabilistic way. Then, the sigmoid function is used for non-linearly transformation of the prediction score, and the original matrix decomposition problem is converted into two successive user latent factors and item latent factor optimization problem. After that, random noise perturbation is added to the objective function to make the algorithm satisfies differential privacy protection. Experiments are carried out on data sets movielens100k, movielens1m, and Yahoo Music. Compared with the existing relevant algorithms, the algorithm improves the F1 value index by 9.29%, 7.40% and 3.61% respectively. Theoretical analysis and experimental results show that the algorithm can effectively guarantee the accuracy of recommendation results while realizing user implicit feedback data protection, and has good application value.

Key words: implicit feedback, matrix factorization, differential privacy preservation, recommendation system