dc.contributor.author |
Ranasingha, SS |
|
dc.contributor.author |
Silva, T |
|
dc.contributor.editor |
Abeysooriya, R |
|
dc.contributor.editor |
Adikariwattage, V |
|
dc.contributor.editor |
Hemachandra, K |
|
dc.date.accessioned |
2024-03-22T05:40:07Z |
|
dc.date.available |
2024-03-22T05:40:07Z |
|
dc.date.issued |
2023-12-09 |
|
dc.identifier.citation |
S. S. Ranasingha and T. Silva, "Personalized Mood-Based Song Recommendation System Using a Hybrid Approach," 2023 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2023, pp. 66-71, doi: 10.1109/MERCon60487.2023.10355387. |
en_US |
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/22375 |
|
dc.description.abstract |
Music recommendation systems are becoming a crucial
concern for the music industry because of the rise of digitization and
the subsequent increase in music consumption. Music applications
continuously strive to enhance their recommendation systems to
ensure that users have an exceptional listening experience and remain
loyal to their platform.
In the early days, the recommendation system used collaborative
filtering and content-based approaches to achieve this goal, but these
approaches have an issue with a cold start, and context awareness of
these approaches is less. Researchers identified in the context of the
personalization of songs, Emotion, and mood can play a huge role.
Research has shown that a user's current emotional state significantly
influences their musical preferences in the short term. Therefore, the
recommendation system moves toward mood-based recommendation
approaches. The vast variety and context-dependent character of the
data that must be considered present the main difficulty for moodbased
recommendation systems. This information can vary greatly and
is depending on several variables, including the user's environment
and personal circumstances.
Hybrid approaches have shown very good results in this domain.
Therefore, in this paper, we are proposing a hybrid approach for a
mood-based personalized song recommendation system. This
approach combines content-based and context-based approaches
together. The proposed solution produces the output as a personalized
song recommendation for the music listener. This output is determined
by several parameters including user mood, the profile of the user, and
history of previously listened to songs. This solution impacts all the
stakeholders. it improves the quality of service of music streaming
platforms and improves the user experience. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/10355387 |
en_US |
dc.subject |
Collaborative approach |
en_US |
dc.subject |
Content-based approach |
en_US |
dc.subject |
Hybrid approach |
en_US |
dc.title |
Personalized mood-based song recommendation system using a hybrid approach |
en_US |
dc.type |
Conference-Full-text |
en_US |
dc.identifier.faculty |
Engineering |
en_US |
dc.identifier.department |
Engineering Research Unit, University of Moratuwa |
en_US |
dc.identifier.year |
2023 |
en_US |
dc.identifier.conference |
Moratuwa Engineering Research Conference 2023 |
en_US |
dc.identifier.place |
Katubedda |
en_US |
dc.identifier.pgnos |
pp. 66-71 |
en_US |
dc.identifier.proceeding |
Proceedings of Moratuwa Engineering Research Conference 2023 |
en_US |
dc.identifier.email |
surajsampath25@gmail.com |
en_US |
dc.identifier.email |
thusharip@uom.lk |
en_US |