# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 5 Experiments
### Ablation Study
#### 5.4.2 Meta-Path Embedding Adjustment  
From the above component adjustment experiment, it can be observed that the effect of the first source of model improvement does not result in a significant effect. Our analysis indicates that the reason for this is that the embedding of the selected meta-path from the dataset becomes saturated. In order to verify this hypothesis, the simulation experiment described in this section was designed to simulate whether the GCN can simulate the embedding of meta-paths in the case of insufficient meta-paths in the dataset (i.e., the embedding of meta-paths is not saturated), expand the range of feature distribution for the meta-paths embedded in the sample, and improve the recommendation effect. The design of the analog element path ablation experiment is described below.  
Assuming that each dataset only provides one meta-path, a comparative experiment was conducted to investigate whether GCN can be used to simulate the embedding of meta-paths without considering the weight aggregation of GAT embedding. This experiment adopted a unified meta-path; the type of meta-path was User-Item-User and the training rate was 80%. The model that does not use GCN to simulate the embedding of meta-paths is named HGE-CRec\({}_{None-OneMP}\), the model embedded by GCN analog meta-path is named HGE-CRec\({}_{GCN-OneMP}\), and the results of the analog element path ablation experiment for the four datasets are shown in Table 9.  
The results of this experiment verify that in the case of insufficient meta-paths the use of a graph convolution network to aggregate neighbor information to generate more analog meta-path embedding can improve the experimental results. The method of adding small sample data to GCN can generate more embedded data for recommendation, improving the accuracy of small-sample training data in the final recommendation process. This  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Metrics** & **HGE-CRec\({}_{GCN}\)** & **HGE-CRec\({}_{GAT}\)** & **HGE-CRec** \\ \hline \hline Scholat & MAE & 0.4528 & 0.4435 & 0.4435 \\  & RMSE & 0.6598 & 0.6295 & 0.6294 \\ \hline CNPC & MAE & 0.8775 & 0.8658 & 0.8658 \\  & RMSE & 1.1667 & 1.1362 & 1.1361 \\ \hline \hline Yelp & MAE & 0.8479 & 0.7807 & 0.7804 \\  & RMSE & 1.1111 & 0.9880 & 0.9884 \\ \hline \hline Movielens & MAE & 0.7103 & 0.6992 & 0.6992 \\  & RMSE & 0.9272 & 0.8981 & 0.8980 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of ablation study on component adjustment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Metrics** & **HGE-CRec\({}_{None-OneMP}\)** & **HGE-CRec\({}_{GCN-OneMP}\)** \\ \hline \hline Scholat & MAE & 0.4559 & 0.4533 \\  & RMSE & 0.6719 & 0.6604 \\ \hline CNPC & MAE & 0.8808 & 0.8795 \\  & RMSE & 1.1784 & 1.1682 \\ \hline \hline Yelp & MAE & 0.8804 & 0.8529 \\  & RMSE & 1.1607 & 1.1283 \\ \hline \hline Movielens & MAE & 0.7136 & 0.7112 \\  & RMSE & 0.9360 & 0.9298 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of meta-path embedding enhancement.  
further shows that the HGE-CRec proposed in this paper can be applied to datasets with insufficient meta-paths. In addition, the traditional predefined meta-path depends on the manual definition provided by researchers. In order to find a better predefined meta-path, a large number of experiments need to be carried out. HGE-CRec effectively expands the range of the feature distribution of the meta-path embedding sample by using GCN to generate more simulated meta-path embedding. For cases with only a small number of meta-paths, the best effect that can be achieved by manually predefined meta-paths can be obtained. From this point of view, the proposed model is conducive to relieving the complexity and uncertainty of manually predefined meta-paths.