# Induced Emotion-Based Music Recommendation through Reinforcement Learning
## 7 Conclusions
#### Limitations and Future Works of the Project  
There are a series of envisioned steps for the next future of _Moodify_ that we try to summarize as follows. Currently, MoodifyWeb interacts with Spotify by searching specific music tracks and recommending them to the user, but we have planned to directly \"incorporate\" it in the Spotify interface (e.g., Spotify plug-in). By doing so, the user does not have to switch between MoodifyWeb and Spotify but can use one only integrated application. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Recommendation**} & **Appropriateness of the** & \multirow{2}{*}{**User Satisfaction**} & **System** \\  & **Recommendations** & & **Responsiveness** \\ \hline \#1 & 4.15 & 4.25 & 4.45 \\ \#2 & 4.20 & 4.15 & 4.05 \\ \#3 & 3.85 & 4.15 & 3.90 \\ \#4 & 4.20 & 4.00 & 3.95 \\ \#5 & 4.75 & 4.15 & 4.10 \\ \#6 & 4.15 & 4.05 & 4.25 \\ \#7 & 3.95 & 3.90 & 4.15 \\ \#8 & 4.75 & 4.15 & 4.05 \\ \#9 & 4.10 & 4.15 & 3.90 \\ \#10 & 4.75 & 4.30 & 4.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the Testing phase across all participants (average scores). Furthermore, we have planned to make _Moodify_ more extensive by considering other aspects that could influence the listener's emotion, such as the environment in which the user activity is carried out while listening to music. At the moment, the system is designed to consider only the starting and target emotions. It could be helpful to add more context to the recommendations, as it happens--with some variations--in context-based filtering techniques. This contextual information may come from the ambient and the type of activity the user is involved in, e.g., gym [20] where the trajectory for inducing a specific emotion could be different than the one used when at home. Of course, this kind of improvement will need extensive study and validation with final stakeholders. Another future direction is represented by the extension of _Moodify_ so to collect and analyze users' behavioral information, e.g., interactions with a mobile or IoT device [20; 59; 60], to better tailor the songs recommended and include the implicit feedback typical of collaborative filtering mechanisms. Different studies have found a connection between emotions and the way we use smartphones [61; 62; 63; 64; 65]. Currently, MoodifyWeb always asks for explicit feedback from listeners. Based on the insights coming from the literature, we will extend the system so that the emotions captured from smartphone interaction will provide implicit feedback. For instance, if we capture sadness through the smartphone interaction while listening to a song, MoodifyWeb could avoid asking for explicit feedback and apply the sad emotion to that song, adjusting the RL method's trajectory appropriately. Finally, to face the problem of the Q-Learning scaling (see Section 4.3.4), as a future development, we are going to exploit Deep Q-learning techniques, which utilize the virtues of deep learning with so-called Deep Q-networks, i.e., feed-forward neural networks used for computing Q-value. Conceptualization, A.G. and R.Z. ; methodology, A.G. and R.Z. ; software, A.G. and R.Z. ; validation, R.D.P. and R.Z. ; formal analysis, R.Z. ; investigation, R.Z. ; resources, D.M. ; data curation, D.M. ; writing--original draft preparation, R.Z. and A.G.; writing--review and editing, A.G., D.M. and R.D.P. ; visualization, D.M. and A.G.; supervision, R.D.P. and D.M. ; project administration, R.Z. and D.M. ; All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Informed Consent Statement: Informed consent was obtained from all participants involved in the study. In this preliminary study, we did not collect users' data or build a dataset. Emotional states are private for each participant using _Moodify_, which does not store them after the listening sessions. Instead, the user assessments on _MoodifyWeb_ are available in the paper.