Music Recommendation Based on Subjective Attributes - Jain, Priyank; Patchava, Vamsikrishna (International Journal of Computer Sciences and Engineering, 2025)
Majority of the music recommendation systems in use today use historical preferences of other users having similar taste to recommend songs to a particular user. Other systems use past preferences of the current user and musical attributes of songs to make recommendations. In this paper, we use a novel approach to recommend music to users based on content-based filtering. This system can be used both as a search engine and for making recommendations. Moreover, this system does not suffer from the cold start problem which most of the recommender systems suffer from. Our system has a very small learning curve. We present a simple yet fast approach to make music recommendations using echonest’s music attributes. The system is based on calculating the Euclidean distance to find out top recommended songs. This system can be used in combination with traditional recommendation systems for more effective recommendation. We think music users will find this system easy to use and experiment with and therefore helpful to discover new music. This system will result in increased enjoyment of music for users.