An Automated Platform for evaluating the factors related to Music Recommendation System

DOI

10.2991/978-94-6463-250-7_2How to use a DOI?

Keywords

1. MRS-Music Recommendation System; 2. YT-YouTube; 3. CF: -Collaborative Filtering; 4. CBM: - Content-Based Model; 5. EDM: -Electronic Dance Music; 6. CART: -Classification Tree

Abstract

Listening to music has become one of the most frequently resorted to pastimes of people ranging from the youth to the elder. While there are umpteen songs of different genres and artists from yesteryears in the podcast, it becomes essential that there is a recommendation System that analyzes the liking of a specific user with the help of the datasets genre and artists of the songs that he/she listened to in the past three days. The goal of this project is to create a system for recommending music that will analyze user interactions with the app or music platform in order to establish their musical preferences. Our system learns from users’ previous listening history and recommends music they want to listen to in the future. Currently music service providers have generic, mood-based playlists, that are the same for all users. Here, we suggest improvements to these playlists by offering custom playlists for each user based on user input. Rich web application technologies have proliferated as a result of the rise in Internet usage as a source of information. Users can use these devices to listen to music without having to download it to their PC. Some people additionally employ their preferred methods to enhance the user experience.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - A. Sheik Abdullah
AU  - M. K. Thamaraimanian
AU  - R. Priyadarshini
AU  - D. Altrin Lloyd Hudson
AU  - V. Naga Pranava Shashank
PY  - 2023
DA  - 2023/10/17
TI  - An Automated Platform for evaluating the factors related to Music Recommendation System
BT  - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
PB  - Atlantis Press
SP  - 3
EP  - 7
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-250-7_2
DO  - 10.2991/978-94-6463-250-7_2
ID  - Abdullah2023
ER  -