## 4 Experiment
### Performance Evaluation  
Tables 2-4 demonstrate the experimental results of the MIMA algorithm and its baselines across three datasets. As shown in the tables, we find that all evaluation metrics of the MIMA model, including Recall@K, Precision@K, and NDCG@K, are significantly better than those of all baselines models. Compared with the highest values of other algorithms, the MIMA model improves the NDCG, precision, and recall performance indicators on the Amine dataset by an average of 4.72%, 3.15%, and 4.70%, respectively. On the MovieLens-1m dataset, the performance metrics of NDCG, precision, and recall are improved by an average of 3.13%, 3.75%, and 4.71%, respectively. The average performance improvements in terms of NDCG, precision, and recall reach 8.65%, 5.16%, and 8.60%, respectively, on the Amazon-Book dataset. Meanwhile, the highest performance improvement percent exceeds 10%. These experimental results mean that the approach proposed in this paper is feasible and effective. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Algorithm** **Recall20** & **Recall30** & **Recall50** & **Precision10** & **Precision20** & **NDCG20** \\ \hline ItemCF & 0.0286 & 0.0304 & 0.0412 & 0.0260 & 0.0201 & 0.0115 \\ MF & 0.0354 & 0.0446 & 0.0644 & 0.0311 & 0.0231 & 0.0214 \\ NCF & 0.0402 & 0.0542 & 0.0685 & 0.0342 & 0.0256 & 0.0256 \\ NGCF & 0.0434 & 0.0580 & 0.0772 & 0.0358 & 0.0294 & 0.0294 \\ PUP & 0.0482 & 0.0574 & 0.0784 & 0.0336 & 0.0296 & 0.0304 \\ PinSage & 0.0429 & 0.0553 & 0.0756 & 0.0329 & 0.0272 & 0.0295 \\ \hline ATGCF & 0.0535 & 0.0616 & 0.0806 & 0.0372 & 0.0308 & 0.0320 \\ ATGCN & 0.0539 & 0.0621 & 0.0815 & 0.0377 & 0.0313 & 0.0322 \\ \hline HetGNN & 0.0552 & 0.0607 & 0.0833 & 0.0476 & 0.0359 & 0.0341 \\ HAN & 0.0547 & 0.0598 & 0.0841 & 0.0452 & 0.0343 & 0.0335 \\ \hline MIMA & **0.0566** & **0.0653** & **0.0910** & **0.0489** & **0.0372** & **0.0357** \\ \hline Improvement & **2.5\%** & **5.2\%** & **8.2\%** & **2.7\%** & **3.6\%** & **4.7\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance evaluation on the Amine dataset and improvement compared to the optimal baseline. Compared with the first six benchmark algorithms, which do not consider node attribute information, MIMA, ATGCF, and ATGCN, which not only use node IDs but also incorporate node attribute features, have better prediction effectiveness. According to the experimental conclusion, it is verified that it is feasible and effective to integrate node attribute information into feature extraction to improve recommendations. Meanwhile, MIMA, HetGNN, and HAN integrate meta-path semantic features of nodes in the aggregation layer for heterogeneous graphs and achieve better recommendation performance. And MIMA obtains better performance than HetGNN and HAN by incorporating node attribute information into the meta-path semantic information aggregation process. The experimental results verify that integrating node attribute information into meta-path aggregation operations can effectively improve algorithm performance. Integrating heterogeneous graphs containing feature nodes, user and item embedded representations incorporating attribute features, and meta-path aggregation operations with feature information into MIMA model training can improve the recommendation effectiveness, enhance the interpretability of recommendation results, and provide a better recommendation model. Meanwhile, a statistical significance analysis, i.e., a \(t\)-test, was conducted on the three datasets. And the \(p\)-values of all three datasets--Amine, MovieLens-1m, and Amazon-Book--were 0.002, \(1.8\times 10^{-17}\), and \(6.2\times 10^{-6}\), respectively. The results of the \(t\)-tests indicate that the improvements are statistically significant for \(p<0.005\). Overall,