一种权衡性能与隐私保护的推荐算法

Aiming at the problem of privacy protection in recommender systems, a balanced optimization model between privacy protection parameters and recommendation accuracy is proposed. Taking online learning resource recommender system as an example, this paper builds a matrix factorization model and studies the relationship between privacy protection parameters and recommendation accuracy after introducing differential privacy noise into data input module and model training module. According to the implicit feedback characteristics of online learning resource recommender system data, a negative sampling algorithm by resource-popularity is proposed. The experiment is based on the original and balanced data from the Network College of Xi'an Jiaotong University using Baidu PaddlePaddle platform, and the root mean square error is used as an evaluation index to measure the recommendation accuracy. The results show that negative sampling makes higher prediction accuracy than the original. And the recommendation prediction accuracy is directly proportional to the reciprocal of differential privacy protection parameter. When RMSEs are less than 2.0 and 1.3 and the privacy protection parameter is 7 and 3, respectively, both the input-based algorithm and the model-based algorithm achieve best balance. Moreover, when the privacy protection parameter is no more than 5, the model-based algorithm has a higher recommendation accuracy than the input-based algorithm.