Abstract
Due to the increase in technology and research over the past few decades, music had become increasingly available to the public, but with a vast selection available, it becomes challenging to choose the songs to listen too. From research done on music recommendation systems (MRS), there are three main methods to recommend songs; context based, content based and collaborative filtering. A hybrid combination of the three methods has the potential to improve music recommendation; however, it has not been fully explored. In this paper, a hybrid music recommendation system, using emotion as the context and musical data as content is proposed. To achieve this, the outputs of a convolution neural network (CNN) and a weight extraction method are combined. The CNN extracts user emotion from a favorite playlist and extracts audio features from the songs and metadata. The output of the user emotion and audio features is combined, and a collaborative filtering method is used to select the best song for recommendation. For performance, proposed recommendation system is compared with content similarity music recommendation system (CSMRS) as well as other personalized music recommendation systems.
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Omowonuola, V., Wilkerson, B., Kher, S. (2023). Hybrid Context-Content Based Music Recommendation System. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_8
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