MSPLD: Shilling Attack Detection Model Based on Meta Self-Paced Learning

With the dramatic rise of recommendation systems, more and more companies use them to improve users' experience. However, the openness of the recommender systems makes them vulnerable to shilling attacks, which causes a bad impact on user satisfaction. Existing shilling attack detection models usually have problems in solving the noise of samples and labels. To this end, this paper proposes a shilling attack detection model based on meta self-paced learning, named MSPLD. Meta self-paced learning can make the model select samples from easy to difficult in the learning process, which can alleviate the problem that the model parameters are difficult to optimize due to the outliers or noises in the samples. Specifically, MSPLD adopts some methods to get the extraction of potential feature embedding vectors first. Second, metadata is selected adaptively by a regression method. Then, it uses the embedding vectors of malicious users and metadata as input data. Third, it optimizes the age loss function and the loss function of the classifier itself with bilateral optimization. The relationship between age and sample loss of the classifier will determine the weight of sample selection. Finally, using the tendency of the age gradually to select training samples from easy to difficult can improve the generalization ability of the models. Compared with the state-of-the-art detection models, the experimental results on two public datasets show that MSPLD can achieve better detection performance. Besides, we illustrate the training process of MSPLD to analyze the reason for the superiority of the model.