## 5 Experiment
### Performance Comparison (RQ1)  
We conducted comparative experiments on two real recipe datasets and evaluated the overall performance of all methods, as shown in Table 2, Figures 4 and 5. The analysis of the results allows us to draw the following observations:  
* Our proposed MKCLN model has the best performance in both datasets. The results show that the fine-grained final representations of users and recipes can be better captured through contrastive learning of embedding representations of three views at two levels, so that the final nodes contain more comprehensive information to the extent of having better recommendation results.
* In the baseline methods, CKE performs better than BPR, which proves the superiority of graph data in recommendations. The results of simple embedding using heterogeneous graphs still perform better than the collaborative filtering methods. This is because they do not pay enough attention to the relevant attributes of the recipes.
* The path-based representative method Metapath2 vec has the worst performance among all methods. This is because the method relies heavily on manual definition of meta-paths and it is difficult to define the best meta-path. This also shows that structural information is crucial in the embedding of heterogeneous graphs.
* The results of the GNN-based baseline methods KGCN, KGAT, and FP-MGCN outperform other baseline methods, which indicates that the high-quality representations of nodes are very important in recommendation, and the higher-order structural and semantic information in the graph is also very valuable for the potential preferences of users.
* The performance of FP-MGCN is better than KGCN and KGAT because FP-MGCN performs embedding and information propagation for nodes through multiple views separately. This shows that embedded propagation through multiple views is valuable for improving recommendation performance.
* Our proposed MKCLN shows better results than the FP-MGCN in the baseline approach, which illustrates the benefit of self-supervised learning in improving recommendation quality and the importance of the fine-grained representation of nodes on recommendation results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Ta-da**} & \multicolumn{2}{c}{**Allrecipes**} \\ \cline{2-5}  & **AUC** & **F1** & **AUC** & **F1** \\ \hline BPR & 0.8267 & 0.7829 & 0.7337 & 0.6281 \\ \hline CKE & 0.8389 & 0.7942 & 0.7585 & 0.6384 \\ \hline Metapath2 vec & 0.7665 & 0.7405 & 0.6320 & 0.5515 \\ \hline KGCN & 0.8818 & 0.7980 & 0.7612 & 0.6532 \\ \hline KGAT & 0.8898 & 0.8177 & 0.7671 & 0.6638 \\ \hline FP-MGCN & 0.9010 & 0.8225 & 0.7718 & 0.6667 \\ \hline MKCLN & 0.9184 & 0.8382 & 0.7882 & 0.6798 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of approaches on Ta-da, Allrecipes.  
In the above comparison test, we explore the methods suitable for recipe recommendations. The results of the Ta-da dataset are better than the Allrecipes dataset when compared to the same metric in both datasets. This is because the Ta-da dataset is of higher quality than Allrecipes, which contains recipes from all over the world and also faces some problems with featured ingredients or recipes that have cold attributes and fewer interactions.