## 5 Conclusions  
The data visualization of BI has significant value in the business field and can effectively guide industrial production and plan business goals. If we can solve some of the inconveniences that analysts encounter when using the data visualization of BI, we can effectively help to improve their analysis results. Due to the consideration of the characteristics of data visualization, the recommendation models applied to them should pay more attention to recommending high-scoring items compared with traditional recommendation models. The model proposed in this paper has been specifically designed based on the characteristics of analysts when using BI for data visualization. Node sampling uses importance sampling, which is performed through spatial importance (spatial key nodes) and logical importance (preference weights), and a propagation and aggregation method combining layer-by-layer feature transmission and a skip connection is used. Combined with a slow-acting collaborative filtering algorithm, the PreferSAGE-SCF model is established. The experiment proved that the model has good performance in recommending data visualization charts and other similar scenarios that require inductive recommendations based on user preferences. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Hit** & **F0.5-Score** & **NDCG** \\ \hline No-Skip-Connection & 0.631 & 0.164 & 0.286 \\ No-Prefer & 0.634 & 0.159 & 0.281 \\ PreferSAGE & 0.651 & 0.165 & 0.288 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: PreferSAGE ablation experiment results on dataset m1-1m. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Hit** & **F0.5-Score** & **NDCG** \\ \hline PreferSAGE & 0.665 & 0.165 & 0.288 \\ PreferSAGE-UserCF & 0.666 & 0.170 & 0.288 \\ PreferSAGE-SCF & 0.666 & 0.171 & 0.291 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model ablation experiments results on dataset m1-1m. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Hit** & **F0.5-Score** & **NDCG** \\ \hline PreferSAGE & 1.000 & 0.633 & 0.698 \\ PreferSAGE-UserCF & 0.960 & 0.516 & 0.605 \\ PreferSAGE-SCF & 1.000 & 0.648 & 0.707 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model ablation experiments results on dataset DA. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Hit** & **F0.5-Score** & **NDCG** \\ \hline No-Skip-Connection & 1.000 & 0.627 & 0.691 \\ No-Prefer & 1.000 & 0.565 & 0.646 \\ PreferSAGE & 1.000 & 0.633 & 0.698 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: PreferSAGE ablation experiment results on dataset DA. Certainly, there are also some inconveniences in the practical application of this recommendation model applied to BI. The first issue is data collection. In order to determine users' preferences during the experimental phase, an eye tracker can be used; charts can be displayed one by one or offline surveys can be conducted. They are not practical for actual use. Therefore, the determination of charts that users are using and collecting usage data is an issue that needs to be considered. This paper proposes that starting from the current position of the mouse and the position of the chart on the current screen can roughly determine the user's usage, or the BI backend can automatically record the user's click on the charts, which is another option. The second issue is the fusion of recommendation results. The following points need to be considered: displaying on a separate dashboard or integrating with a regular dashboard, where to integrate into the page, in what form to display, and so on. These points require further detailed investigation and design, and finding better hybrid recommendation models is also a direction that requires further research. If the recommendation results are combined with charts generated by other methods to dynamically adjust or generate the analysis page after correctly handling the above issues, they will have practical significance for improving the final analysis effect of analysts. Conceptualization, S.T., Y.Y. and L.Y.