1 Department of CSE (AI and ML), Techno Main Salt Lake, Salt Lake, Sector - V, Kolkata - 700091, West Bengal, India.
2 Department of Computer Application, Techno India Kolkata, Salt Lake, Sector - V, Kolkata - 700091, West Bengal, India.
3 Department of CSE-CS and DS, Brainware University, Barasat, Kolkata - 700125, West Bengal, India.
International Journal of Science and Research Archive, 2025, 15(03), 660-666
Article DOI: 10.30574/ijsra.2025.15.3.1780
Received on 01 April 2025; revised on 08 June 2025; accepted on 10 June 2025
The digital music industry has undergone a paradigm shift, favoring streaming services and personalized recommendation systems. This paper presents one unique Online Music Recommendation System, a web-based music library and recommendation platform that allows users to listen to, share, and recommend songs among friends. The system integrates social interactions to enhance music discovery while keeping the user interface intuitive and accessible. The application is not only follows a client-server architecture but also allows administrators to manage music content but also enables users to interact through friend lists and song recommendations. This experimental implementation shows a high level of user engagement and satisfaction, validating the feasibility and effectiveness of socially-enhanced music recommendation platforms.
Recommendation System; Web Application; Socially-Driven; Observation Metric; Feedback; User Rating
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SUDIPTA CHAKRABARTY, LABONI SAHA and SUBHARANJAN BASU. Pulse: An online music recommendation system for socially-driven music discovery. International Journal of Science and Research Archive, 2025, 15(03), 660-666. Article DOI: https://doi.org/10.30574/ijsra.2025.15.3.1780.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
