Abstract
Music plays a vital role in the life of several people, and they consider it as a part of their life. Whenever a person is happy, sad or emotional, he prefers to relax his mind by listening to music. To get songs of their own interest, users keep searching for them in search engines. As we look into the history of searching, the complexity of search has gradually decreased, maybe due to advancement in technology and various methods adopted for searching. In this paper, we are concentrating on suggesting appropriate songs for the users based on their feelings (or mood) known as the music recommendation system. The objective of the paper is to find the suitable method for providing recommendations based on access to the music by similar users and history. Here, we are considering different methods for implementation like cosine similarity, collaborative filtering, popularity-based and emotion-based methods and also many parameters like singer, name of the song, genre and movies which help in finding proper song. We are also analyzing the performance of the same. The advantage of the music recommendation system is that it avoids the user from searching manually. It not only saves time for searching, but also updates the similar new song, if any.
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Aparna, R., Chandana, C.L., Jayashree, H.N., Hegde, S.G., Vijetha, N. (2022). Emotion and Collaborative-Based Music Recommendation System. In: Hemanth, D.J., Pelusi, D., Vuppalapati, C. (eds) Intelligent Data Communication Technologies and Internet of Things. Lecture Notes on Data Engineering and Communications Technologies, vol 101. Springer, Singapore. https://doi.org/10.1007/978-981-16-7610-9_59
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DOI: https://doi.org/10.1007/978-981-16-7610-9_59
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