# A Continuous Music Recommendation Method Considering Emotional Change
## 6 Experiments and Evaluation
### Experimental Results and Evaluation
#### 6.3.3 Evaluation of Recommendation Results Based on User Emotions  
To assess the recommendation results based on user emotions, a recommendation list was created following the procedures outlined in Section 6.3.2. The similarity between the music emotion vector and user emotion vector was calculated for each time of the day. Considering that the emotional history data were collected at 1 h intervals, a set of 10 songs was recommended by sorting them in descending order of similarity. For experimental evaluation, the similarities between the actual emotion vector, emotion vector using MMM, emotion vector using PMM, and music vector were computed.  
Tables 15 and 16 show the comparison of content recommendation results. These represent the recommendation results for the corresponding user at times \(t_{0}+1\) and \(t_{0}+2\). As a result of comparing each recommendation result, it can be confirmed that PMM includes more music in the recommendation results based on actual values  
Precision, recall, F1-score, and balanced accuracy were measured to evaluate the recommendation results based on user emotions. The recommendation list based on the actual emotion vector was compared with that based on the predicted emotion vector to calculate these metrics. Table 17 presents the precision, recall, and F1-score as evaluation results for the recommendation outcomes.  
As observed from the comparison with actual emotional data, PMM demonstrates higher precision, recall, and F1-score values compared with MMM, indicating its effectiveness in recommending music suitable for users.  
Table 18 and Figure 15 illustrate the overall average balanced accuracy of the recommendation results using MMM and PMM. The top ten recommendation lists were extracted and compared based on the actual emotion vector, emotion vector using MMM, and emotion vector using PMM. The comparison shows that PMM achieves a higher balance accuracy of 26.88% compared with MMM.  
Additionally, to assess the accuracy of the recommendation list, the similarity between the recommended music vector and the actual value was calculated. Table 19 and Figure 16 depicts a comparison of the average similarity of the recommendation results obtained using PMM and MMM. The comparison confirms that PMM can recommend music better suited to the user's actual emotions compared with MMM.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Actual Values**} & \multicolumn{2}{c}{**MMM**} & \multicolumn{2}{c}{**PMM**} \\ \hline
**Rank** & **Music** & **Similarity** & **Rank** & **Music** & **Similarity** & **Rank** & **Music** & **Similarity** \\ \hline
1 & M510 & 0.999 & 1 & M538 & 0.9318 & 1 & M496 & 0.9995 \\
2 & M550 & 0.9967 & 2 & M497 & 0.9001 & 2 & M530 & 0.9976 \\
3 & M541 & 0.9944 & 3 & M436 & 0.8998 & 3 & M513 & 0.9975 \\
4 & M455 & 0.9926 & 4 & M520 & 0.8968 & 4 & M473 & 0.9969 \\
5 & M530 & 0.9814 & 5 & M464 & 0.8960 & 5 & M518 & 0.9949 \\
6 & M496 & 0.9769 & 6 & M555 & 0.8913 & 6 & M503 & 0.9943 \\
7 & M513 & 0.9746 & 7 & M450 & 0.8912 & 7 & M455 & 0.9919 \\
8 & M462 & 0.9737 & 8 & M501 & 0.889 & 8 & M545 & 0.9907 \\
9 & M518 & 0.9609 & 9 & M430 & 0.8873 & 9 & M442 & 0.9907 \\
10 & M473 & 0.9570 & 10 & M507 & 0.8849 & 10 & M550 & 0.9898 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparison of recommendation results at time \(t_{0}+2\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-score**} \\ \cline{2-7}  & **MMM** & **PMM** & **MMM** & **PMM** & **MMM** & **PMM** \\ \hline \(t_{0}+1\) & 0.2777 & 0.6722 & 0.2777 & 0.6766 & 0.2777 & 0.6744 \\ \(t_{0}+2\) & 0.2733 & 0.6255 & 0.2733 & 0.6311 & 0.2733 & 0.6288 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Comparison of average precision, recall, and F1-score for the recommendation results.