# Sequential Movie Genre Prediction Using Average Transition Probability with Clustering
## 4 Experimental Results
### Results
#### 4.3.2 Effects of Subgenre Trimming  
In order to maximize the advantages of clustering, we come up with a method of trimming the subgenres that are not preferred in the cluster. To see this, we set the threshold \(\eta\) by 0.5 and if there is a cluster that does not exceed 0.5 at any one of Recall, Precision, Accuracy, and F1 score, it is subject to trimming. We select the trimming threshold based on the empirical result after the training process. We conducted several tests to derive an appropriate threshold value. During the experiment, we found that the metrics for the worst performance cluster subject to trimming have an approximate value of 0.5 for the used data. We have checked that if the threshold value is lower than this value, even a cluster requiring trimming is not subject to trimming, so a significant change in performance cannot be confirmed. It is included as the trimming target. We have checked that that if the threshold value is lower than this value, no significant change in performance can be confirmed because even clusters requiring trimming are not subject to trimming. If the threshold value is increased, clusters that do not require trimming (with a clear preference) are included as trimming targets. For the result, three cases were considered: before clustering, after clustering, and after trimming for two types of training data, namely [Genre, ATV] and [Genre*ATV], for each model. As shown in Figure 12, most of the metrics for the aftertrimming case have larger values than the others, except for precision. Figure 11: Result of clustering for RNN (**a–c**), LSTM (**d–f**), and GRU (**g–i**), respectively. Figure 12: Result of subgenre trimming for RNN (**a,b**), LSTM (**c,d**), and GRU (**e,f**), respectively. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ctrimming** & **Recall (1, 2)** & **Precision (1, 2)** & **Accuracy (1, 2)** & **F1 Score (1, 2)** \\ \hline BT (mean) & 0.53 & 0.48 & 0.69 & 0.71 & 0.84 & 0.82 & 0.59 & 0.56 \\ BT (worst) & 0.35 & 0.36 & 0.69 & 0.60 & 0.79 & 0.77 & 0.46 & 0.45 \\ AT (worst) & 0.43 & 0.37 & 0.70 & 0.63 & 0.82 & 0.80 & 0.53 & 0.47 \\ AT (mean) & 0.58 & 0.49 & 0.69 & 0.72 & 0.85 & 0.84 & 0.63 & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Four performance results of GRU with training data type [Genre*ATV]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Clustering** & **Recall (1, 2)** & **Precision (1, 2)** & **Accuracy (1, 2)** & **F1 Score (1, 2)** \\ \hline BC & 0.45 & 0.46 & 0.76 & 0.66 & 0.85 & 0.82 & 0.57 & 0.54 \\ AC (best) & 0.75 & 0.92 & 0.79 & 0.84 & 0.91 & 0.94 & 0.77 & 0.88 \\ AC (worst) & 0.27 & 0.40 & 0.84 & 0.61 & 0.84 & 0.77 & 0.41 & 0.61 \\ AC (mean) & 0.51 & 0.56 & 0.75 & 0.68 & 0.86 & 0.84 & 0.59 & 0.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Four performance results of LSTM with training data type [Genre*ATV]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Clustering** & **Recall (1, 2)** & **Precision (1, 2)** & **Accuracy (1, 2)** & **F1 Score (1, 2)** \\ \hline BC & 0.49 & 0.45 & 0.70 & 0.69 & 0.83 & 0.82 & 0.58 & 0.54 \\ AC (best) & 0.73 & 0.89 & 0.75 & 0.87 & 0.89 & 0.93 & 0.74 & 0.88 \\ AC (worst) & 0.35 & 0.36 & 0.69 & 0.60 & 0.79 & 0.77 & 0.46 & 0.45 \\ AC (mean) & 0.53 & 0.48 & 0.69 & 0.71 & 0.84 & 0.82 & 0.59 & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Four performance results of GRU with training data type [Genre*ATV]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Clustering** & **Recall (1, 2)** & **Precision (1, 2)** & **Accuracy (1, 2)** & **F1 Score (1, 2)** \\ \hline BC & 0.44 & 0.40 & 0.77 & 0.74 & 0.85 & 0.84 & 0.56 & 0.52 \\ AC (best) & 0.73 & 0.82 & 0.80 & 0.71 & 0.91 & 0.80 & 0.76 & 0.76 \\ AC (worst) & 0.29 & 0.29 & 0.87 & 0.67 & 0.85 & 0.72 & 0.43 & 0.40 \\ AC (mean) & 0.52 & 0.47 & 0.77 & 0.71 & 0.86 & 0.83 & 0.58 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Four performance results of RNN with training data type [Genre*ATV].