# A Continuous Music Recommendation Method Considering Emotional Change
## 7 Conclusions and Future Research  
With ongoing advancements in emotion-based recommendation research, services that recommend various types of content to users are being developed. In such emotion-based recommendation systems, predicting the user's emotional state can enhance satisfaction with the recommended content. However, current research on content recommendation often focuses solely on the user's current emotions without considering changes in individual emotion states. Figure 16: Comparison of average similarity of recommendation results. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**Similarity**} \\ \cline{2-3}  & **MMM** & **PMM** \\ \hline \(t_{0}+1\) & 0.7937 & 0.9482 \\ \(t_{0}+2\) & 0.7398 & 0.9097 \\ Avg. & 0.7668 & 0.9290 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Comparison of average similarity of recommendation results. Figure 15: Comparison of overall average balanced accuracy for recommendation results. In this study, we propose a method for continuous music recommendation that reflects emotional changes. First, we construct emotion vectors for music using valence and arousal values, which are elements of [PERSON]'s emotional model, which were categorized in four emotional areas. Using a multiple regression model, we explored the relationships between valence, arousal, and these four emotions to generate music emotion vectors. Second, we developed a user emotion vector based on emotional changes using the user's historical emotional data. These vectors were updated over time, and an individual's emotional state transition matrix was created using a Markov chain process. A PMM was defined, and the user's emotion vector was generated by predicting the emotional state at time \(t_{0}+n\). Third, we implemented a method to continuously recommend content reflecting emotional changes. To create a recommendation list, the similarity between the content emotion vector and the user emotion vector was calculated using cosine similarity. In addition, we used an interpolation method to estimate the emotional state between times \(t_{0}+n\), generating a recommendation list according to the estimated emotional state. To validate the effectiveness of the proposed method, we assessed the results of music emotion analysis, user emotion prediction considering emotional changes, and music recommendation based on user emotions. The music emotion analysis indicated an average error rate across all emotions of 12.74%, achieving an accuracy of 87.26%. To enhance the accuracy of our evaluations, it would be beneficial to utilize more precise and reliable evaluation metrics. In a comparative experiment, we evaluated user emotion prediction using an MMM and our PMM, which adapts the mental model from existing research. Predicting emotions at times \(t_{0}+1\) and \(t_{0}+2\) using the MMM and PMM revealed that the PMM improved the error rate by 13.68% over the MMM, increasing accuracy. Additionally, in the evaluation of music recommendation experiments, PMM demonstrated a higher balanced accuracy of 26.88% compared with MMM. The similarity between the recommended music vector and the actual emotion vector was higher with PMM. These findings confirm our ability to predict user emotions accurately and recommend music that aligns closely with user preferences. It is anticipated that satisfaction with the music provided can be enhanced by continuously recommending music according to the user's emotions and situations. Beyond music, this approach is expected to extend its utility to aiding in product recommendations and psychological treatments by reflecting users' emotional changes. The limitations and future research of this study are as follows. First, this study lacks longitudinal validation over extended periods, making it limited in assessing the model's performance in real-world scenarios where user emotions evolve more dynamically.