User interests change over time. If the same level of privacy protection is used for data of all periods in the recommender systems, it is easy to introduce unnecessary noise and reduce data utility. Therefore, a differen? tial privacy protection recommendation algorithm based on the time weight factor is proposed. The algorithm first de? signs a time weight factor to measure the importance of data and then allocates the different privacy budgets to the data according to the time weight factor. That is, different intensity of privacy protection is performed on the data in different periods. Moreover, a probability matrix factorization model based on differential privacy is constructed for a personalized recommendation. Experimental results show that the proposed algorithm can preserve data utility more effectively and improve the accuracy of recommendation results under the condition of privacy protection.