## 6 Conclusions and Future Work  
Starting from solving the balance between accuracy and time of the recommendation system model, this paper explores various factors affecting the complexity of the model, innovatively adds the \"gate\" structure on the basis of the framework of the Wide & Deep series model, and proposes the GAFM model, which has achieved considerable results. Specifically, we divided the factors affecting the model complexity into data complexity and model structure complexity, and added the methods to solve the two kinds of problems into the two \"gates\", respectively, adjusting the data scale and model structure, and achieving the balance between prediction accuracy and speed. We put our model on three data sets of different sizes and scenarios for horizontal comparison test, and the final experiment proved that compared with the existing model, GAFM model has significant advantages in both accuracy and speed.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Baselines** & **Improvements** \\ \hline Wide \& Deep & +2.76\%\&\& + 7.58\% \\ \hline PNN & +2.44\%\&\& + 4.93\% \\ \hline AFM & +1.17\%\&\& + 24.09\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance growth of GAFM compared to other models.  
In the future work, we hope to realize automatic learning of the model to find the optimal parameter g to adapt to different application scenarios, because it is troublesome to adjust the parameters repeatedly and it may not be able to find the best results, so automatic learning is a very necessary technology, which may need to rely on the relevant knowledge of reinforcement learning. Secondly, we plan to optimize the model structure of GAFM so that it can be significantly better than PNN model in time and AFM model in accuracy, making GAFM more convincing in a variety of application scenarios. Finally, we will continue to explore other potential factors affecting the complexity of the model, and discuss its reliability, whether it fits the GAFM model, and if it does, we will find ways to add it to the existing model to further improve the efficiency of the model.  
Conceptualization, H.Y.; methodology, H.Y.; software, H.Y.; validation, H.Y.; formal analysis, H.Y.; writing--original draft preparation, H.Y.; writing--review and editing, Y.L.; supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 61971268 and the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No.19 YJC760049).  
Not applicable.  
The datasets in this study are available online at UCI Machine Learning Repository: Adult Data Set and MovieLens | GroupLens.  
We thank the National Natural Science Foundation of China for funding our work, grant number 61971268 and the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No.19 YJC760049).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
PNN Product-based Neural Network  
CNN Convolutional Neural Network  
RNN Recurrent Neural Network  
FNN Factorization Neural Network  
Deep FM Deep Factorization Machine  
NFM Neural Factorization Machine  
AFM Attentional Factorization Machine  
RELU Rectified Linear Unit