Music Recommendation System

The digital music streaming services have changed the way people find and listen to music. Nowadays, with millions of songs to choose from, it can be difficult to choose the music that best suits your taste. Music recommendation systems have become an essential part of these platforms. They are desi...

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Bibliographic Details
Main Authors: D, Shyam Prakash, A, Jyothir B, M, Raja Santhosh, P, Prithivi Raj, M, Mithun Raj
Format: Conference Proceeding
Language:English
Subjects:
Ethics
Machine learning
Measurement
Mood
Music
Real-time systems
Recommender systems
Shape
Technological innovation
User experience
Online Access:Request full text
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Summary:The digital music streaming services have changed the way people find and listen to music. Nowadays, with millions of songs to choose from, it can be difficult to choose the music that best suits your taste. Music recommendation systems have become an essential part of these platforms. They are designed to improve user experience by providing personalized music recommendations. In this abstract, we will provide a detailed description of our music recommendation system. The system uses advanced machine learning (ML) and data analysis techniques to provide personalized music recommendations. The system uses collaborative filtering, content-based filtering and hybrid recommendation approaches. In addition, the system takes into consideration contextual factors (e.g., user demographic, listening history, real-time behavior) to refine its suggestions. Our proposed music recommendation system is rigorously evaluated using large datasets and various metrics (e.g. accuracy, diversity, serendipity, etc.). We also consider the ethical implications of our recommendation algorithms to ensure fairness and transparency in our recommendations.
ISSN:2469-5556
DOI:10.1109/ICACCS60874.2024.10716833