## 3 Results
### Results
#### 3.2.1 Overall Performance  
Table 4 shows the performance of our model and those of other methodologies. HR@k and NDCG@k were calculated for k = 5, 10, and 20. Our model had an embedding size of 128, a batch size of 1024, and a running rate of 0.001. The bold numbers in the table represent the best performances among the methodologies.  
We inferred the following facts:  
* Our model showed sufficient performance on the ml-Small data. In HR@5, it showed similar performance to BPR, FISM, and NAIS; however, it showed better performance in HR@10 and HR@20. The algorithms utilizing graph structure showed good performance.
* The performance of our model on the ml-1m dataset was also sufficient. It showed a relatively low performance in metrics at rank 5, but for those at ranks 10 and 20, the difference was low. The performance of the graph-based algorithms is relatively poor.
* The performance of the algorithms showed significantly different patterns on the two datasets. Traditional methodologies exhibited relatively better performance on the ml-1m dataset, whereas the performance of HS-GCN, RKGE, and our methodology, which utilize graph structural information, actually decreased. We can infer that this is due to the characteristics of the data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ml-Small**} & \multicolumn{4}{c}{**ml-1m**} \\ \cline{2-13}  & \multicolumn{2}{c}{**HR**} & \multicolumn{2}{c}{**NDCG**} & \multicolumn{2}{c}{**HR**} & \multicolumn{2}{c}{**NDCG**} \\ \cline{2-13}  & **@5** & **@10** & **@20** & **@5** & **@10** & **@20** & **@5** & **@10** & **@20** & **@5** & **@10** & **@20** \\ \hline itemPOP & 0.188 & 0.292 & 0.408 & 0.122 & 0.158 & 0.186 & 0.311 & 0.455 & 0.638 & 0.208 & 0.255 & 0.301 \\ itemKNN & 0.304 & 0.405 & 0.518 & 0.230 & 0.263 & 0.290 & 0.368 & 0.521 & 0.707 & 0.255 & 0.306 & 0.351 \\ BPR & 0.341 & 0.450 & 0.563 & 0.229 & 0.262 & 0.284 & **0.510** & **0.687** & **0.843** & 0.349 & **0.405** & **0.443** \\ FISM & 0.327 & 0.415 & 0.527 & 0.223 & 0.252 & 0.276 & 0.482 & 0.658 & 0.815 & 0.331 & 0.388 & 0.425 \\ NAIS & 0.336 & 0.424 & 0.531 & 0.234 & 0.250 & 0.276 & 0.497 & 0.671 & 0.826 & **0.397** & 0.397 & 0.435 \\ HS-GCN & **0.380** & **0.477** & **0.675** & **0.257** & **0.280** & **0.347** & 0.452 & 0.616 & 0.774 & 0.323 & 0.380 & 0.418 \\ RKGE & 0.365 & 0.461 & 0.630 & 0.248 & 0.275 & 0.345 & 0.435 & 0.623 & 0.784 & 0.318 & 0.377 & 0.408 \\ Ours & **0.335** & **0.473** & **0.618** & **0.226** & **0.270** & **0.307** & **0.438** & **0.628** & **0.796** & **0.288** & **0.350** & **0.392** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall Performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Number of Properties** & **Examples** \\ \hline Writing & 39 & [PERSON], [PERSON], [PERSON] \\ \hline MovieBudget & 8 & Budget\_Under\_norm\_30\%, Budget\_Under\_norm\_100\%, Budget\_Under\_norm\_40\% \\ \hline MoviePopularity & 7 & Popularity\_Under\_norm\_100\%, Popularity\_Under\_norm\_90\%, Popularity\_Under\_norm\_80\% \\ \hline MovieCountry & 11 & United States, United Kingdom, France \\ \hline MovieVote\_Count & 6 & \
\begin{tabular}{c} Vote\_count\_Under\_norm\_100\%, \\ Vote\_count\_Under\_norm\_50\%, \\ Vote\_count\_Under\_norm\_60\% \\ \end{tabular} \\ \hline Crew & 12 & [PERSON], [PERSON], [PERSON] \\ \hline MovieRuntime & 9 &
\begin{tabular}{c} Runtime\_Under\_norm\_50\%, \\ Runtime\_Under\_norm\_60\%, \\ Runtime\_Under\_norm\_100\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._