# Induced Emotion-Based Music Recommendation through Reinforcement Learning
## 2 Related Work
### Context-Based Filtering  
The context-based filtering approach takes advantage of the public perception of a music track in its suggestions. It exploits social media such as Facebook and Twitter and video platforms such as YouTube to collect information and derive insights about the public opinion of songs. Then, it recommends such music tracks accordingly to the users. This approach considers the users' listening history of collecting user data; next, it recommends similar songs based on the engagement the songs have generated on social media. The context-based technique can build a \"For You section\" for the user through intelligent exploitation of user preferences (i.e., the listening history) and social media engagement of different music tracks. Another technique in this category of filtering uses the user's location to suggest appropriate music tracks. The basic idea is that listeners in the same place may like similar music, and the system suggests music tracks with this assumption. The literature offered insights into the performance of this model, that is, it could perform as well as the amount of social information collected [30], but it needs to integrate with various sources and exploit a joint analysis of a massive data load to ensure good performance.  
A different kind of context-based technique exploits data captured from the users, for example, from their activities that are treated as context. In [31], the authors propose a smartphone-based mobile system to recognize human activities and recommend music accordingly. In the proposed method, a deep recurrent neural network is applied to obtain a high level of activity recognition accuracy from accelerometer signals on the smartphone. Music recommendation is performed using the relationship between recognized human activities and the music files that reflect user preference models in order to achieve high user satisfaction. The results of comprehensive experiments with real data confirm the accuracy of the proposed activity-aware music recommendation framework. In this case, the authors have not developed the system as an application for end-users, and they have not evaluated their method with listeners. Conversely, in the present work, we provide insights from end-users on the MoodifyWeb app deployed. Similarly, in [21], the author proposed a framework based on deep learning and IoT architectures to build a music recommendation system, but did not provide any software or evaluation to listeners. Both the aforementioned works revolve around the recognition of emotion through different devices and the recommendation of a suitable song. Differently, we aim to induce emotion through a series of songs with _Moodify_.  
With respect to context-based filtering, our solution does not build a listening history nor collect information to be used for the recommendation. Instead, the listening history is implicitly employed in the training phase to build the agent and the reward of our method. The only listening information exploited concerns the audio features from Spotify of the songs listened to during the training sessions.