# A Continuous Music Recommendation Method Considering Emotional Change
## 2 Related Research  
### Emotion-Based Recommendations  
Recently, with the growth of the OTT market and the production of various types of content, technologies that recommend customized content to users have been advancing. As user participation in service and product development increases alongside recommendation technology, the emotions experienced by users become crucial factors in their service utilization and purchasing decisions [4]. Consequently, research in emotional engineering, which analyzes and evaluates the relationship between human emotions and products, is actively progressing. Emotional engineering embodies engineering technologies aimed at faithfully reflecting human characteristics and emotions, with emotional design, emotional content, and emotional computing serving as integral research fields within this discipline [4]. Moreover, with the proliferation of mobile devices and the increased use of social networking services, individuals can share their thoughts and opinions anytime and anywhere. Consequently, user emotions are analyzed through social media platforms, and numerous studies are underway on recommendations based on user emotions. Content such as music, movies, and books has a significant influence on changing users' emotions. Therefore, emotions are an important factor in content recommendation. Existing emotion-based recommendation studies analyze content and user emotions to suggest content that aligns with the user's emotional state. The music recommendation method that considers emotions [5, 44, 45, 46] recognizes facial expressions to derive the user's emotions and recommends music suitable for the user. As a recommendation method, content-based filtering (CSF), collaborative filling (CF),and similarity techniques were used to recommend music suitable for the user. In addition, studies have been performed for recommending music by estimating emotions through brainwave data, which is a user's biosignal [47], recommending music by analyzing the user's music listening history data [48], recommending music by analyzing the user's music listening history data [48], and even studying the user's keyboard input and mouse click patterns to analyze and recommend music [49]. Research on music recommendations that consider existing emotions mainly recommends music based on the user's current emotional state. The emotion-based movie recommendation method [6] defines colors as singular emotions and recommends movies based on the color selected by the user. This approach combines content-based filtering, collaborative filtering, and emotion detection algorithms to enhance the movie recommendation system by incorporating the user's emotional state. However, because colors are defined as representing singular emotions and consider only the user's emotion corresponding to the selected color, the accuracy of the recommendation results is expected to be low. To provide recommendations that better suit the user, it is essential to express the diverse characteristics and intensity of the user's emotions. The emotion-based tourist destination recommendation method [7] combines content-based and collaborative filtering techniques to recommend tourist destinations based on the user's emotions. This approach involves collecting data on tourist attractions and quantifying emotions by calculating term frequencies based on words from eight emotion groups. Furthermore, research has been conducted on recommending artwork by considering emotions [8], recommending fonts by analyzing emotions through text entered by users [9], and recommending emoticons [10]. A common limitation of these emotion-based content recommendation studies is that they make recommendations that consider only the user's current emotions. An individual's emotions change over time, and even the same emotion can be felt differently depending on how it changes. An individual's current emotional state may transition to a different emotional state over time or depending on the situation.