Personalized mood-based song recommendation system using a hybrid approach

Abstract

Music recommendation systems are becoming a crucial concern for the music industry because of the rise of digitization and the subsequent increase in music consumption. To ensure that users have an exceptional listening experience and remain loyal to their platform, music applications are continuously striving to enhance their recommendation systems. In the early days, the recommendation system used collaborative filtering and content base approaches to achieve this goal, but these approaches have an issue with a cold start, and context awareness of these approaches is less. Researchers identified in the context of the personalization of songs, Emotion, and mood can play a huge role. Research has shown that a user's current emotional state significantly influences their musical preferences in the short term. Therefore, the recommendation system moves toward mood-based recommendation approaches. The vast variety and context-dependent character of the data that must be considered present the main difficulty for mood-based recommendation systems. This information can vary greatly and is depending on a number of variables, including the user's environment and personal circumstances. Hybrid approaches have shown very good results in this domain. Therefore, in this master thesis, we are proposing a hybrid approach for a mood-based personalized song recommendation system. This approach combines content-based and context-based approaches together. The proposed solution produces the output as a personalized song recommendation for the music listener. This output is determined by several parameters including user mood, the profile of the user, and history of previously listened to songs.