# Temporal Enhancement of Top-N Recommendation on Heterogeneous Graphs
## 6 Conclusions  
In this study, we propose a model for temporally enhanced top-N recommendation on HINs called TMRec for effectively capturing the temporal relations and heterogeneous information of users, items, and meta-paths. Firstly, we adopted long short-term memory (LSTM) and residual self-attention (RSN) to process users and items and enhance the system's ability to both learn the heterogeneous information in them and capture the temporal relations among them. Secondly, we designed a novel method for processing meta-paths, which includes deep perception self-attention (DPSA), max -pooling, and L\({}_{2}\)-normalization. It can effectively obtain the temporal relations among different nodes in meta-paths. Thirdly, we use collaborative attention to process meta-paths, users, and items to obtain their interactions. Finally, extensive experiments were conducted on three public datasets of recommender systems to prove the superiority of our method compared with state-of-the-art top-N recommendation models. Although TMRec can improve the performance of recommender systems, most meta-paths are manually selected, which may hinder the application of the model on different tasks and datasets. In the future, we will conduct research on how to automatically generate meta-paths and introduce this strategy into the recommendation task to further improve performance. For example, we could automatically capture path instances between entities that have different semantics. In addition, we have not considered some important auxiliary information for recommendation, so we will develop a method to obtain much more auxiliary information to boost performance.  
[PERSON] developed the algorithm and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research study was funded by the National Natural Science Foundation of China (Nos. 62174084 and 62341408).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{2}{c}{**Model**} & **TMRec** & **TMRec-DPSA** & **TMRec-LSTM** & **TMRec-RSA** \\ \hline \multirow{4}{*}{Movielens} & Prec@10 & 0.3568 & 0.3527 & 0.3561 & 0.3564 \\ \cline{2-6}  & Recall@10 & 0.2381 & 0.2338 & 0.2354 & 0.2355 \\ \cline{2-6}  & NDCG@10 & 0.7011 & 0.6995 & 0.7002 & 0.7007 \\ \hline \multirow{4}{*}{LastFM} & Prec@10 & 0.4933 & 0.4869 & 0.4912 & 0.4917 \\ \cline{2-6}  & Recall@10 & 0.5186 & 0.5149 & 0.5173 & 0.5177 \\ \cline{2-6}  & NDCG@10 & 0.8681 & 0.8642 & 0.8630 & 0.8639 \\ \hline \multirow{4}{*}{Yelp} & Prec@10 & 0.1851 & 0.1805 & 0.1823 & 0.1827 \\ \cline{2-6}  & Recall@10 & 0.6497 & 0.6436 & 0.6471 & 0.6475 \\ \cline{1-1} \cline{2-6}  & NDCG@10 & 0.6485 & 0.6442 & 0.6453 & 0.6458 \\ \hline \end{tabular}
\end{table}
Table 3: The results of the ablation studies.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** We would like to thank Nanjing University for providing the necessary technical support.  
**Conflicts of Interest:** The authors declare no conflicts of interest.