基于时间权重因子的隐私保护推荐算法

用户兴趣是随时间变化的，若对推荐系统中所有时间段的数据均采用同等程度的 隐私保护，容易引入不必要的噪声，降低数据效用 . 为此，提出一种基于时间权重因子的差分 隐私保护推荐算法. 首先，设计时间权重因子，用于衡量数据重要性. 然后，根据时间权重因子 划分隐私预算，对不同时间段的数据施加不同强度的隐私保护. 在此基础上，构建基于差分隐 私的概率矩阵分解模型，用于完成个性化推荐. 实验结果表明，该算法在满足隐私保护的条件 下，能够更有效地保留数据效用，提高推荐结果的准确性.

Privacy Protection Recommendation Algorithm Based on Time Weight Factor

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.