## 4 Experiment
### Ablation Studies  
To further evaluate the performance of our framework, we experimented with partially modified models. In the previous experiments, the original DeepAnT and RNN-AE models were trained separately for each test-user. Whereas DeepAnT used prediction loss while RNN-AE used reconstruction loss, we applied the improvements of the loss function and GM to the original model separately in the ablation experiments to demonstrate the effectiveness of the improvements in both aspects. The model structure and loss function of the DeepAnT+CLL and RNN+CLL models were changed based on the original DeepAnT and RNN models in Section 3.5. The prediction/reconstruction loss functions of these models were modified to CLL. We kept their training and testing methods unchanged. They were still trained and tested separately on each test-user. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**AI Model**} & \multicolumn{5}{c}{**Dataset**} \\ \cline{3-6}  & & **Commodity** & **Eshop** & **Yoochoose** & **Taobao** \\ \hline ABOD [14] & & 0.710 & 0.560 & 0.358 & 0.471 \\ CBLOF [13] & & 0.721 & 0.582 & 0.716 & 0.509 \\ DAE & ✓ & 0.654 & 0.634 & 0.822 & 0.651 \\ ECOD [19] & & 0.805 & 0.720 & **0.936** & 0.523 \\ iForest [16] & & 0.662 & 0.550 & 0.543 & 0.495 \\ LODA [18] & & 0.461 & 0.478 & 0.838 & 0.505 \\ LOF [12] & & 0.634 & 0.561 & 0.807 & 0.505 \\ OCSVM [15] & & 0.703 & 0.606 & 0.836 & 0.505 \\ PCA [17] & & 0.802 & 0.588 & 0.729 & 0.487 \\ DeepAnT [24] & ✓ & 0.578 & 0.586 & 0.814 & 0.640 \\ RNN-AE [21] & ✓ & 0.659 & 0.604 & 0.827 & 0.609 \\ Our Framework DeepAnT & ✓ & 0.897 & **0.919** & 0.871 & 0.851 \\ Our Framework RNN & ✓ & **0.907** & 0.905 & 0.879 & **0.878** \\ \hline \hline \end{tabular}  
* The average AUC score was calculated as follows: the AUC score for detecting anomalous actions for each user was calculated and then averaged across users. The check mark indicates that the model is an artificial intelligence model. Bold indicates that the model performed best on that dataset.  
\end{table} 
Table 3: Average AUC score 1 of models. The DeepAnT+GM and RNN-AE+GM models were trained according to Section 3.6, and we used the same testing approach as with our framework models. Since they still used the prediction loss and the reconstruction loss, respectively, there was no need to generate comparative anomaly data for training. The models were trained on normal behavior records generated from training-users and then tested using test-user data. The test set was the same as the one for testing the model based on our framework, and the anomalous data were similarly inserted. Figures 7 and 8 show the results. On the Commodity dataset, changing the loss function worked much better than introducing GM. This is because the GM was introduced to solve the problem of insufficient single-user behavior data. The number of users in the Commodity dataset was large; thus, the effect of introducing GM was not that significant. As a comparison, on the Eshop dataset, the amount of behavior data of each user was small, so introducing GM resulted in greater improvement compared to introducing CLL. On the Yoochoose dataset, because using the data of a single user is sufficient to determine the behavior of the user according to the ECOD model in Section 4.3, the introduction of GM had no effect. In addition, owing to the limited number of each user's behavior segments, it was impossible to train using a sufficient number of negative samples, so the introduction of CLL slightly improved the performance. Thereafter, the AUC score was improved when a large amount of contrastive learning was performed based on the GM. On the Taobao dataset, the model performed worse after the addition of GM, while the addition of both GM and CLL achieved a great improvement. This indicates that GM alone cannot help the model extract features when the dataset feature extraction is difficult.