# Induced Emotion-Based Music Recommendation through Reinforcement Learning
## 2 Related Work
### Collaborative Filtering  
Collaborative filtering generates automatic predictions about users' interests by collecting preferences from a large user base. This approach adopts _user ratings_ to recommend songs. Such systems are built on the following assumption: users who rate songs similarly in the past will continue to rate them similarly in the future [24]. Usually, clustering algorithms are employed to deliver recommendations. Ratings can be of two kinds: _explicit_ or _implicit_. Examples of explicit ratings are the stars-based systems that e-commerce sites use: the user has to express a rating often based on a five-star score where the higher is the better (e.g., Trustpilot (Available online: [[https://it.trustpilot.com](https://it.trustpilot.com)]([https://it.trustpilot.com](https://it.trustpilot.com)), accessed on 18 October 2022)). These ratings are explicitly provided by the users. Instead, implicit ratings can be collected by throwing light on the user's behaviors. For instance, play counts can be employed for implicit rating: a song played thousands of times gets a higher implicit rating than some others that have listened a dozen times. The biggest drawback of these kinds of systems is that they offer poor recommendations in the early stages. Especially for songs with very few ratings, recommendations are performed in a not-very-reliable [25] fashion. This is a well-known issue in the literature, named the _cold-start_ problem. When a new user joins the system, the recommender cannot offer effective suggestions, as the user has never interacted before and hence has not rated anything yet. Another challenge of collaborative filtering is related to human effort. In general, users are not willing to rate every item on a system that requires a lot of effort and attention to generate recommendations. Among the closest articles in the literature, there is the proposal by [26] in which association rules and music features were added to a collaborative filtering mechanism. The system considers users' preferences for different song features and uses the similarity of interests among different users to suggest music. The system has been implemented in a Web application as we did, and the author also performed an experiment with 20 real users. The main difference is that we do not use a collaborative filtering method, but the suggestion is only tailored to the specific user; we employ an RL-based method to recommend music and not a rule-based algorithm; lastly, the deployed Web application is not described thoroughly as well as the user evaluation, which is, furthermore, only a preliminary one and does not involve, for example, _confirmation of expectation test_. In addition, the results of the evaluation are not clear.  
_Modify_ employs an explicit rating \"encapsulated\" in an RL approach tailored to one user only. Every user has his/her own agent tailored, through usage, to his/her needs. Such a rating mechanism is used to define a reward function, i.e., by asking the user to evaluate how much the emotion felt at the end of each listening is similar to target emotion.