## 1 Introduction  
Business intelligence (BI) is a support system that enhances and improves business decisions and is a collection of enterprise decision support technologies [1]. It can integrate and manage existing data and knowledge of enterprises, providing decision making support for business and management personnel. Data visualization is an important feature of BI, and there are two main ways to use this feature. One is for analysts to interactively query and generate data visualization charts for subsequent analysis work. The other is for chart makers to pre-create analysis charts and integrate them into a dashboard, which is a page containing several or dozens of analysis charts which are then provided to analysts for analysis work. Due to the convenience of the second method for analysts and the ease of access control for administrators, it has become a commonly used method. However, this usage method also has some shortcomings. When analysts browse analysis pages, if the required charts are far apart or not on the same page, there may be situations wherein they need to repeatedly drag up and down the page or constantly switch pages, increasing the workload and interrupting the analysis process. In addition, analysts may sometimes overlook certain key information and charts due to negligence during their analysis work, so a reminder mechanism is needed to reduce the probability of such situations occurring. Therefore, introducing recommendation models into the data visualization of BI can reduce the operational complexity of analysts, alleviate the situation of interrupted thinking, and inspire analysts based on the behavior of other analysts, reducing omissions in the analysis process and improving the analysis effect. In terms of personalized recommendation, early personalized recommendation systems predicted user interests by modeling the frequently visited items, keywords of interest, or identities of users [2; 3]. The continuous advancement of technology has made the design of recommendation models increasingly complex and effective. With the development of computer equipment and technology, deep learning models have become popular, which has also led to the development of recommendations in this direction. Deep learning methods are playing an increasingly important role in recommendation systems for generating low-dimensional embeddings of items such as images, text, and user personal information [4]. The irregularity of data, such as personalized user data or user access records, which have variable length, makes Convolutional Neural Networks (CNNs)) and other neural networks unable to directly perform routine operations such as convolution and pooling. However, utilizing the structure of graphs has natural advantages in processing such data. Graph Neural Networks (GNNs)) extend traditional neural networks to graphs and are a series of neural networks that learn feature patterns from graphs. [PERSON] et al. [5] first used a Recurrent Neural Network (RNN)) to compress the information of data nodes and proposed the concept of GNN. Due to their strong representational power and high interpretability, GNN-based methods have been widely welcomed in the field of recommendation, and many models have been proposed based on this foundation [6; 7]. The most noteworthy among them is that of [PERSON] et al., who applied the idea of a CNN to a graph and proposed the Graph Convolutional Network (GCN) [8], which become one of the most successful deep learning architectures for graph structure learning. Due to the expected advantages of using recommendation models in BI's visual analysis and the advantages of using graph-based models in solving these problems, this paper proposes an inductive heterogeneous graph recommendation model, PreferSAGE-SCF, for high-scoring items, which recommends analysis charts that analysts may be interested in based on their preferences. More specifically, the data displayed in the chart may change at any time, but the attributes of the chart (such as title, style, content, etc.)