# Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization
## 4 Experiments and Analysis
### Automatic Optimization Process of Key Hyperparameters  
We use CNN-SAD-HHO to denote the CNN-SAD method after being improved by the proposed hyperparameter automatic optimization algorithm. That is, CNN-SAD method is used as the \(DLDM\) of Algorithm 2 for automatically optimizing the key hyperparameters. Similarly, we use DL-DRA-HHO to denote the DL-DRA method after being improved by Algorithm 2.  
Except for the key hyperparameters described in Section 4.2, other settings in CNN-SAD-HHO and DL-DRA-HHO are the same as these in the CNN-SAD and DL-DRA, respectively. In Algorithm 2, \(N\) is set to 30 and \(T\) is set to 500 which are the default settings of HHO algorithm.  
The automatic optimization process of key hyperparameters for CNN-SAD-HHO and DL-DRA-HHO when performing Algorithm 2, which uses the training and validation sets of MovieLens 10 M dataset as its input, are shown in Figures 2 and 3, respectively.  
As shown in Figure 2, we record the fitness of all solutions in the automatic optimization process. After the iteration is completed, the optimal solution corresponding to the maximum fitness is (97, linear, 39,12). As shown in Figure 3, after the iteration is completed, the optimal solution corresponding to the maximum fitness is (20, linear, 8, 3).  
The automatic optimization process of key hyperparameters for CNN-SAD-HHO and DL-DRA-HHO when performing Algorithm 2, which uses the training and validation sets of Amazon dataset as its input, are shown in Figures 4 and 5, respectively.  
Figure 3: Automatic optimization process of key hyperparameters for DL-DRA-HHO on MovieLens 10 M dataset.  
Figure 2: Automatic optimization process of key hyperparameters for CNN-SAD-HHO on MovieLens 10 M dataset.  
As shown in Figure 4, after the iteration is completed, the optimal solution corresponding to the maximum fitness is (84, tanh, 87, 25). As shown in Figure 5, after the iteration is completed, the optimal solution corresponding to the maximum fitness is (84, elu, 14, 15).  
As shown in Figures 2 and 4, CNN-SAD method is sensitive to the key hyperparameters. The fitness fluctuates greatly with the change of key hyperparameters. Compared with CNN-SAD, DL-DRA method has better robustness as shown in Figures 3 and 5. The fitness of DL-DRA tends to be more stable. The reason for this phenomenon may be that DL-DRA method, which is an improvement of CNN-SAD method, uses more convolution and pooling layers to construct more reasonable neural network for the detection of recommendation attack.  
Although, the maximum number of iterations \(T\) is set to 500 in our  
CNN-SAD-HHO and DL-DRA-HHO approaches employ Algorithm 2 to automatically optimize the key hyperparameters. With the output of Algorithm 2, the Algorithm 3 is used to generate classifiers and detect the test sets.
2. In the second group, we compare the following representative detection methods with the improved approaches CNN-SAD-HHO and DL-DRA-HHO. * PCA-VarSelect [10]: This is one of the representative works in unsupervised detection methods. In this method, the PCA technique is used to compute the covariance among users for the detection. * SSADR-CoF [18]: This is a representative semi-supervised detection method. The ensemble learning is used to reduce the dependence of the training model on labeled user profiles. * CNN-LSTM [24]: This is a hybrid supervised detection method based on two types of deep neural networks CNN and LSTM.