## 5 Conclusions  
There are differences and similarities between users. Anomalous behavior is also different for each user. This study focused on detecting the anomalous behavior of a single user on trading platforms and is the first to introduce interuser information in the detection of anomalous user action by contrasting learning. To use differences between different users, CLL was designed for unlabeled data. With minor modifications, any neural network sequence model can learn the contrast information between the last and the former actions. To use similarities between users and overcome the underfitting problem, we designed the GM to detect all users, rather than a separate model for each user. The model can also distinguish and adapt the detection for different users. Through experiments, we demonstrated that CLL is more efficient than are other loss functions and that GM improves the effectiveness of the model and greatly reduces the training and testing times, which are indispensable. This suggests that similarities and differences between users should be considered when performing anomaly detection on trading platforms. In addition, we experimentally illustrated that both traditional and neural network anomaly-detection methods can distinguish the behaviors of different users, proving that users have different behaviors. For trading platforms, the security of user property is very important. Using anomaly-detection methods can better protect the security of user property. Considering interuser information, as demonstrated in this paper, can greatly improve the performance of these \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Dataset**} \\ \cline{2-5}  & **Commodity** & **Eshop** & **Yoochoose** & **Taobao** \\ \hline Original DeepAnT & 92.2 & 2.2 & 282.0 & 6357.2 \\ +CLL & 67.4 & 2.7 & 352.3 & 7436.3 \\ +GM & 35.6 & **0.3** & 1.9 & **238.4** \\ +CLL+GM & **28.4** & 0.4 & **0.6** & 426.5 \\ \hline \hline \end{tabular} Bold indicates that the model performed best on that dataset.  
\end{table} 
Table 4: Total time (min) of the DeepAnT model and partially modified DeepAnT models including training and testing time. methods on multiuser trading platforms. These anomaly-detection methods should be used as a loss recovery measure. When an anomalous action is detected, it means that user property may be at risk, and it is recommended that the trading platform notifies the user of the action by email, SMS, etc. However, we only used one kind of negative sample in this study. We hope that based on this study, many contrastive learning anomaly-detection models with more kinds of negative samples can be designed for trading platforms to utilize the interuser information. Conceptualization, Y.L. and P.Y. ; methodology, Y.L. ; software, Y.L. ; validation, Y.L. ; investigation, Y.L. ; resources, P.Y. ; data curation, P.Y. ; writing--original draft preparation, Y.L. ; writing--review and editing, Y.L. ; visualization, Y.L. ; supervision, P.Y. ; project administration, P.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (2019 YFB1405000). Because the commodity trading dataset is from a real trading platform, it cannot be made public for commercial privacy reasons. [PERSON] would like to thank his friends. They have provided him with emotional support and valuable advice. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The following abbreviations are used in this manuscript: