## 4 Experiments  
### Datasets and Evaluation Metrics  
The first experimental dataset used in this paper, DA, comes from our survey of the big data analysis department of a manufacturing enterprise. Due to the lack of built-in data collection capabilities in the data visualization used by the enterprise, the survey method used was to find offline statistics on the recent usage of several visualization analysis dashboards by users. The title, style (chart representation elements such as lists, histograms, line charts, etc. ), and interactivity (whether data filtering can be performed, whether drilling is allowed, etc.) of the analysis charts included in the dashboard were used as chart attributes, and the recent chart usage of users were used as rating data. After sorting and enhancing results, the final dataset contained 2508 rating records of 344 charts from 50 users. The distribution of user charts access is shown in Figure 5. Due to the fact that the model proposed in this paper can also be used for other similar tasks of recommending items with high user preferences, simulation experiments can be conducted using public datasets with user preferences or ratings to verify the effectiveness and universal applicability of the model to larger datasets. This paper selects the \"ml-1m\" [34] dataset as the second experimental dataset, which was published by GroupLens Labs and belongs to the MoveLens dataset series. It contains 1,000,209 rating records of 3883 movies from 6040 users. The structure of this dataset is relatively simple and easy to handle, while meeting the relevant conditions of the model in this paper; thus, this dataset  
Figure 4: Fusion of results, assuming the critical point is 3 (beginning from 0) and the goal is to recommend five items to the user. Green represents the current user, purple represents recommended items that do not participate in collaborative filtering result fusion, white represents recommended items that participate in collaborative filtering result fusion, and gray represents placeholders. is suitable. Due to the purpose of the model in this paper being to recommend high-scoring items to users, a biased test sample extraction method was used to randomly select 30% of the items with the highest score of 40% or above from each user in two datasets, to be used as test data. From the perspective of users as a whole, how many users are hit by the correct results is an important factor that affects their experience. If the hit rate of the model is too low, it will result in some users not receiving any help. For individual users, the accuracy of chart recommendations and the ranking of recommendation results are the most influential factors in their experience. The accuracy determines whether chart recommendations have practical effects, and the correct ranking of recommendation results can greatly improve the user experience. As a contradictory and complementary indicator of accuracy, it is also valuable to consider the recall indicator. Based on the above requirements, this paper uses the widely used evaluation metrics, including Hit, F-score, and NDCG. Hit represent the probability of actual results appearing in the predicted results, that is, whether each user's predicted results contain hit items. If any item hits, it is 1; otherwise, it is 0. The higher the value, the higher the Hit rate of the model. As shown in the formula, where \(\Pi\) is the indicator function, if the condition of this function is 1, the return value of this function is 1; otherwise, it is 0. \[Hit=\frac{1}{N}\sum_{i=1}^{N}\Pi(rank_{i}\leq k) \tag{5}\]  
The precision indicator represents the proportion of true positive cases in the predicted positive cases, and the recall indicator represents the proportion of true positive cases in the predicted results among all true positive cases. The larger these two values, the better the model performance. F-score is a type of evaluation indicator that harmonizes precision and recall.