# Induced Emotion-Based Music Recommendation through Reinforcement Learning
## 1 Introduction  
Music is an important aspect of any human culture, being able to induce a range of intense and complex emotions both in musicians involved in composing pieces and individuals listening to them. The digital age involved sizeable changes in the economy, in the industrial and social spheres, with interesting advances and transformations also in the music field. With regard to the music streaming market, its size was valued at USD 29.45 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 14.7% from 2022 to 2030 [1]. The penetration of music streaming platforms, as well as the ubiquity of smartphones, will boost the music market growth, accordingly. Furthermore, platforms that allow streaming services are gaining popularity, offering services such as song recommendations and automatic playlist personalizations by supporting individuals in suggesting similar (and preferred) pieces. On the other hand, emotion/mood has become a fundamental criterion used by digital technologies in predicting social behaviors or conditioning people in their social interactions and work activities. In light of this, music systems that regulate mood and emotions in our daily life are arousing particular interest. Consequently, a great deal of research has been undertaken in the _affective computing_ community to model the relationship between music and emotion [2; 3; 4; 5]. More in general, applications of affective computing studies can be found in education, health care, entertainment, ambient intelligence, multimedia retrieval, and music retrieval and generation. As for the specific musical context, most of these works consist of context-sensitive recommendation tools that consider the listener's emotional state. Unfortunately, few results have been obtained in the study of music systems for _induction_ of future emotional states, i.e., methods for influencing the emotional state of listeners and adapting interaction with technology to their affective state. Unlike games or movies [6; 7], where the study of the induction of emotional states has recently obtained interesting results, especially in educational or commercial contexts, the potential of music as a means of inducing emotions still leaves significant possibilities for study. Inductive systems use _affective contents_ for induction of emotional states, assuming that the emotions conveyed by the affective content (_perceived_ emotions) are always consistent with emotions brought to mind in users' (_induced_ emotions) games or movies [2]. There are at least two main perspectives in such systems: user and system perspectives. Users perceive and interpret the content (perceived emotions). Systems usually provide _emotional annotations_ that describe which emotions are expected by the users during an interaction step (intended emotions). In general, perceived and induced emotions are not usually considered separately in studies on affective content. However, some studies on \"music emotions\" have shown that in music, traditionally regarded as an art form that can make people produce emotional responses or induce their emotions naturally [8; 9], emotions perceived are not always consistent with the emotions elicited in listeners [10]. This is particularly evident in most music recommendation systems. Recommender systems have been widely studied in recent years [11; 12], but they do not always lead to the best possible designs for affective recommendation systems. Several studies showed that emotions could play a significant role in designing intelligent music recommendation systems, and most of them focused on \"recognizing\" emotions induced by music [13; 14; 15; 16; 17; 18], nearly no attempts have been made to model musical emotions and their changes over time in terms of a target \"emotion to induce\".