  The advent of the information age has led to the problems of information
overload and unclear demands. As an information filtering system, personalized
recommendation systems predict users' behavior and preference for items and
improves users' information acquisition efficiency. However, recommendation
systems usually use highly sensitive user data for training. In this paper, we
use the latent factor model as the recommender to get the list of recommended
items, and we representing users from relevant items Compared with the
traditional member inference against machine learning classifiers. We construct
a multilayer perceptron model with two hidden layers as the attack model to
complete the member inference. Moreover, a shadow recommender is established to
derive the labeled training data for the attack model. The attack model is
trained on the dataset generated by the shadow recommender and tested on the
dataset generated by the target recommender. The experimental data show that
the AUC index of our attack model can reach 0.857 on the real dataset
MovieLens, which shows that the attack model has good performance.
