## 5 Half Convolutional Autoencoder
### Utilizing HCAE in Recommendation Systems
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Auto** & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**P@5**} & \multirow{2}{*}{**P@10**} & \multirow{2}{*}{**R@5**} & \multirow{2}{*}{**R@10**} & \multirow{2}{*}{**Time [s]**} \\  & **Encoder** & & & & & & \\ \hline \multirow{6}{*}{kNN-Content} & AE & 0.7692 & 0.8142 & 0.7906 & 0.4352 & 0.5587 & \\  & (\(-\)_1.09\%_) & (\(+\)_0.68\%_) & (\(+\)_0.79\%_) & (\(+\)_0.55\%_) & (\(+\)_0.68\%_) & \\ \cline{2-8}  & VAE & 0.7680 & 0.8137 & 0.7914 & 0.4358 & 0.5592 & \\  & (\(-\)_0.94\%_) & (\(+\)_0.74\%_) & (\(+\)_0.69\%_) & (\(+\)_0.41\%_) & (\(+\)_0.59\%_) & \\ \cline{2-8}  & DAE & 0.7668 & 0.8147 & 0.7925 & 0.4355 & 0.5588 & \\  & (\(-\)_0.78\%_) & (\(+\)_0.62\%_) & (\(+\)_0.55\%_) & (\(+\)_0.48\%_) & (\(+\)_0.66\%_) & 291 \\ \cline{2-8}  & **HCAE** & **0.7608** & **0.8198** & **0.7969** & **0.4376** & **0.5625** & 297 \\ \hline \multirow{6}{*}{FM\({}_{\text{genome}}\)} & AE & 0.7702 & 0.8052 & 0.7836 & 0.4300 & 0.5571 & \\  & (\(-\)_0.87\%_) & (\(+\)_0.62\%_) & (\(+\)_0.72\%_) & (\(+\)_0.78\%_) & (\(+\)_0.71\%_) & 23,412 \\ \cline{2-8}  & VAE & 0.7688 & 0.8057 & 0.7841 & 0.4295 & 0.5578 & \\  & (\(-\)_0.69\%_) & (\(+\)_0.56\%_) & (\(+\)_0.66\%_) & (\(+\)_0.90\%_) & (\(+\)_0.59\%_) & 23,535 \\ \cline{2-8}  & DAE & 0.7681 & 0.8063 & 0.7852 & 0.4310 & 0.5587 & \\  & (\(-\)_0.60\%_) & (\(+\)_0.48\%_) & (\(+\)_0.52\%_) & (\(+\)_0.55\%_) & (\(+\)_0.43\%_) & 23,447 \\ \cline{2-8}  & **HCAE** & **0.7635** & **0.8102** & **0.7893** & **0.4334** & **0.5611** & 23,503 \\ \hline \multirow{6}{*}{kNN-Content-SVD} & AE & 0.7634 & 0.8162 & 0.7946 & 0.4356 & 0.5575 & \\  & (\(-\)_0.98\%_) & (\(+\)_0.78\%_) & (\(+\)_0.76\%_) & (\(+\)_0.53\%_) & (\(+\)_0.78\%_) & 596 \\ \cline{2-8}  & VAE & 0.7623 & 0.8166 & 0.7941 & 0.4349 & 0.5580 & \\  & (\(-\)_0.84\%_) & (\(+\)_0.73\%_) & (\(+\)_0.82\%_) & (\(+\)_0.68\%_) & (\(+\)_0.69\%_) & 653 \\ \cline{2-8}  & DAE & 0.7612 & 0.8177 & 0.7954 & 0.4355 & 0.5594 & \\  & (\(-\)_0.70\%_) & (\(+\)_0.60\%_) & (\(+\)_0.66\%_) & (\(+\)_0.55\%_) & (\(+\)_0.44\%_) & 603 \\ \cline{2-8}  & **HCAE** & **0.7559** & **0.8226** & **0.8007** & **0.4379** & **0.5619** & 612 \\ \hline \multirow{6}{*}{kNN-Content-SVD++} & AE & 0.7584 & 0.8186 & 0.7959 & 0.4368 & 0.5609 & \\  & (\(-\)_0.90\%_) & (\(+\)_0.78\%_) & (\(+\)_0.90\%_) & (\(+\)_0.66\%_) & (\(+\)_0.69\%_) & 27,687 \\ \cline{2-8}  & VAE & 0.7569 & 0.8191 & 0.7962 & 0.4363 & 0.5610 & \\ \cline{2-8}  & DAE & 0.7560 & 0.8198 & 0.7972 & 0.4374 & 0.5617 & \\ \cline{2-8}  & DAE & (\(-\)_0.58\%_) & (\(+\)_0.63\%_) & (\(+\)_0.73\%_) & (\(+\)_0.52\%_) & (\(+\)_0.55\%_) & 27,698 \\ \cline{2-8}  & **HCAE** & **0.7516** & **0.8250** & **0.8031** & **0.4397** & **0.5648** & 27,737 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the baseline models when utilizing 600-element movie feature vectors generated from AE variants and an HCAE. amount of time if the number of tags increases sharply, which is highly likely to happen because they are freely generated by users. Therefore, the trade-off is totally acceptable.