# Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering
## 4 Analysis of Experimental Data and Results
### Preference Comparison Experiment and Result Analysis  
To investigate the influence of different types of learning resources in the recommendation process, we conducted a comparison between the video-dominant and text-dominant scenarios while maintaining the fusion ratio determined in the previous experiments. Within the specified fusion ratio condition, Top-K recommendations were performed to assess the impact of the two resource types. We also varied the value of \(K\) to compare experimental results across different recommendation quantities. In both sets of experiments, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **F1@13** & **F1@14** & **F1@15** & **F1@16** & **F1@17** & **F1@18** \\ \hline TransD & 0.3048 & 0.3291 & 0.3497 & 0.3716 & 0.3909 & 0.4104 \\ ItemCF-Text & 0.4383 & 0.4859 & 0.5315 & 0.5783 & 0.5879 & 0.6008 \\ ItemCF-Video & 0.4596 & 0.4918 & 0.5437 & 0.5808 & 0.5902 & 0.6086 \\ CKE & 0.4729 & 0.5135 & 0.5511 & 0.6077 & 0.6193 & 0.6322 \\ KGAT & 0.5088 & 0.5451 & 0.5842 & 0.6224 & 0.6476 & 0.6596 \\ Text Dominance & 0.5546 & 0.5858 & 0.6089 & 0.6367 & 0.6638 & 0.6785 \\ Video Dominance & 0.5734 & 0.6005 & 0.6193 & 0.6431 & 0.6637 & 0.6877 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental comparison results. \(K\) ranged from 10 to 16, \(\alpha\) was set to 0.6, and TransD's embedding dimension remained at 100. Each experiment was repeated 10 times, and the average values were computed. The results for different values of \(K\) are detailed in Table 5.  
Based on the above results, we can observe that as the K value increases, the accuracy under the two preference rules shows a decreasing trend, while the recall rate shows an increasing trend. Increasing the k value usually increases the diversity of recommendation results. This means that the system is more inclined to recommend various types of content to meet the diverse needs that users may have, which can improve recall rates. But, capturing more related projects may also lead to the introduction of unrelated projects, thereby reducing accuracy. The comparison of the results shown in the two images shows that assigning high-weight values to video resources has achieved good results in recommendation performance, both in terms of accuracy and recall. This indicates that compared to text resources, video resources play a more important role in course recommendation, and in this course, video resources can better meet users' learning needs and preferences. In the experiment in Figure 5a, a sharp decrease in the accuracy curve was observed when the recommended quantity K increased from 11 to 12. When increasing the k value, the system may have already considered most of the relevant courses. When more course options are introduced, there may be differences in the relevance between these additional courses and the specific needs of the user. This sharp decrease may be due to one or more additional recommended courses not matching the user's interests, resulting in a decrease in accuracy. Meanwhile, when increasing the number of recommendations, users may face information overload issues as they need to evaluate more course options. This may make it more difficult for users to make wise choices, thereby affecting accuracy. However, as the experiment progresses, the accuracy curve stabilizes. Users gradually adapt to the  
Figure 5: (**a**) Comparison of precision under different preference rules; (**b**) Comparison of recall rates under different preference rules.