A Survey of Music Recommendation System for old age people
Samya Das1 , Souvik Sikdar2 , Soham Dey3 , Radha Krishna Jana4
Section:Survey Paper, Product Type: Journal Paper
Volume-11 ,
Issue-01 , Page no. 214-220, Nov-2023
Online published on Nov 30, 2023
Copyright © Samya Das, Souvik Sikdar, Soham Dey, Radha Krishna Jana . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Samya Das, Souvik Sikdar, Soham Dey, Radha Krishna Jana, “A Survey of Music Recommendation System for old age people,” International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.214-220, 2023.
MLA Style Citation: Samya Das, Souvik Sikdar, Soham Dey, Radha Krishna Jana "A Survey of Music Recommendation System for old age people." International Journal of Computer Sciences and Engineering 11.01 (2023): 214-220.
APA Style Citation: Samya Das, Souvik Sikdar, Soham Dey, Radha Krishna Jana, (2023). A Survey of Music Recommendation System for old age people. International Journal of Computer Sciences and Engineering, 11(01), 214-220.
BibTex Style Citation:
@article{Das_2023,
author = {Samya Das, Souvik Sikdar, Soham Dey, Radha Krishna Jana},
title = {A Survey of Music Recommendation System for old age people},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {11 2023},
volume = {11},
Issue = {01},
month = {11},
year = {2023},
issn = {2347-2693},
pages = {214-220},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1436},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1436
TI - A Survey of Music Recommendation System for old age people
T2 - International Journal of Computer Sciences and Engineering
AU - Samya Das, Souvik Sikdar, Soham Dey, Radha Krishna Jana
PY - 2023
DA - 2023/11/30
PB - IJCSE, Indore, INDIA
SP - 214-220
IS - 01
VL - 11
SN - 2347-2693
ER -
Abstract
One of the most fruitful forms of media is music since it can evaluate strong emotions and marshal listeners with subliminal instructions. It manipulates our feelings, which in turn affects how we feel. Books, movies, and television are a few other ways to communicate, but music communicates its message in just a few brief seconds. It can encourage us and help us when we are down. We frequently experience a mood when listening to depressing music. We experience happiness when we listen to music. Many Internet businesses have looked for using sentiment analysis to recommend content that is in keeping with the human emotions that are represented in informal texts posted on social networks. Here we propose a music recommendation methodology.
Key-Words / Index Term
Collaborative filtering, Content based Filtering, Recommendation System.
References
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