# Temporal Enhancement of Top-N Recommendation on Heterogeneous Graphs
## 5 Experiments
### Experimental Results Analysis
From the above results, we can find that the co-attention mechanism is more suitable for paths composed of different types of nodes, while the self-attention mechanism is more useful for paths composed of nodesof the same type. In addition, although GraFC2T2 considers the temporal information among different entities, its ability to capture heterogeneous information is limited. Therefore, the proposed TMRec achieves better performance than GraFC2T2 because of important components such as LSTM, RSA, and DPSA. 4. We can see from Table 2 that Prec@10, Recall@10, and NDCG@10 are increased by 0.0406, 0.0395, and 0.0448, respectively, compared with the average values of the comparison models on the LastFM dataset and by 0.0359, 0.0323, and 0.0412, respectively, on the Movielens dataset. These experimental results demonstrate that the proposed TMRec has better performance in these scenarios. In contrast to these two datasets, Prec@10, Recall@10 and NDCG@10 are increased by 0.0359, 0.0323, and 0.0412, respectively, compared with the average values of the comparison models on the Yelp dataset. Although TMRec's advantage over the other models is relatively lower on the Yelp dataset, it still exhibits better recommendation performance. In order to further demonstrate the effectiveness of the proposed TMRec, we also carried out some experiments on the DUNN dataset on TOP-10 by using the comparison models. Additionally, we used the F-score to evaluate their performance on TOP-10. The experimental results are shown in Figure 3.  
We can see from Figure 3 that the F-score of the proposed TMRec model is 0.8097. It achieves the highest F-score, indicating that it has the best performance among the comparison models. In addition, it is evident that the reduction in the standard deviation from ItemKNN to TMRec reflects an improvement in the stability of the proposed model. This is particularly important in real-world applications where consistent performance is crucial. In order to show the advantage in time complexity of the proposed TMRec, we investigated the training and inference time of MCRec, GraFC2T2, MEGNN, and TMRec, as shown in Figure 4.  
We can see from Figure 4 that the proposed TMRec has a lower time cost than GraFC2T2 and MEGNN, which demonstrates its advantage in time complexity. In addition, although TMRec has a higher time cost than MCRec, it shows better performance. In summary, the experimental results indicate that TMRec has better time complexity and superior performance. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Movielens**} & \multicolumn{3}{c}{**LastFM**} & \multicolumn{3}{c}{**Yelp**} \\ \cline{2-10}  & **Prec@10** & **Recall@10** & **NDCG@10** & **Prec@10** & **Recall@10** & **NDCG@10** & **Prec@10** & **Recall@10** & **NDCG@10** \\ \hline ItemKNN & 0.2578 & 0.1536 & 0.5692 & 0.4160 & 0.4513 & 0.7981 & 0.1386 & 0.5421 & 0.5378 \\ \hline BPR & 0.3011 & 0.1946 & 0.6459 & 0.4129 & 0.4492 & 0.8099 & 0.1403 & 0.5651 & 0.5531 \\ \hline MF & 0.3247 & 0.2053 & 0.6511 & 0.4364 & 0.4634 & 0.7921 & 0.1481 & 0.5991 & 0.6011 \\ \hline NCF & 0.3256 & 0.2165 & 0.6682 & 0.454 & 0.4678 & 0.8104 & 0.1490 & 0.6001 & 0.6007 \\ \hline HAN & 0.3356 & 0.2155 & 0.6845 & 0.4720 & 0.4978 & 0.8421 & 0.1602 & 0.6123 & 0.6134 \\ \hline MCRec & 0.3415 & 0.2213 & 0.6876 & 0.4770 & 0.5028 & 0.8491 & 0.1674 & 0.6312 & 0.6300 \\ \hline GraFC2T2 & 0.3400 & 0.2181 & 0.6866 & 0.4764 & 0.4991 & 0.8433 & 0.1652 & 0.6212 & 0.6274 \\ \hline MEGNN & 0.3410 & 0.2214 & 0.6861 & 0.4773 & 0.5018 & 0.8490 & 0.1670 & 0.6305 & 0.6305 \\ \hline TMRec & 0.3568 & 0.2381 & 0.7011 & 0.4933 & 0.5186 & 0.8691 & 0.1851 & 0.6497 & 0.6485 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with state-of-the-art models.