Main Article Content
There is an astounding increase in music digitally, available. The fundamental objective of musical recommendations is to propose songs that are appropriate to the tastes of the user. Content-based filtration and content-based approaches are currently most recommended by Streaming Music systems. Songs based on But the Cold-Start problem fails in these systems. This paper provides user-based hybrid algorithms for music recommending systems to address the problem of Cold-Start by providing context conscious and tailored musical recommendations to new and existing users depending on their own context. We have developed, implemented and analyzed music recommendation systems with several algorithms in this project. The music advice is a highly complicated subject since it is necessary to structure music so that the favorite songs are recommended to users that will not be defined. Practical tests by real users assessed the offered algorithms and framework satisfactorily.
How to Cite
content-based filtering, streaming history, collaborative filtering, Hybrid recommendation, Deep learning, music recommendations
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