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Saranya Maneeroj. Policy GRU-RL : simplified music playlist recommendation using sequential on reinforcement learning concept. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2024.
Policy GRU-RL : simplified music playlist recommendation using sequential on reinforcement learning concept
Name: Chanapa Chanarong
Organization :
Chulalongkorn University. Faculty of Science
Email :
chanapa parn@hotmail.com
Name: Saranya Maneeroj
Organization :
Chulalongkorn University. Faculty of Science
Email :
maneeroj poo@hotmail.com
LCSH:
Music
-- Computer-assisted instruction
LCSH:
Reinforcement learning
Abstract:
In the realm of streaming services, recommendation
systems play a crucial role in meeting user preferences by aiding
them in discovering music tailored to their tastes. Reinforcement
learning (RL) stands out as a popular method for music
recommendations. Nevertheless, prior approaches have grappled
with the challenge of over-fitting. After a certain learning period,
the agent may struggle to predict actions solely based on past
interactions, posing issues for the current user context. To address
this limitation, previous methods must be retrained by resetting
all parameters in the agent. This study introduces the Policy
GRU-RL method, which combines sequential-based learning and
reinforcement learning to tackle over-fitting without the necessity
of resetting all parameters. This method capitalizes on the
features of a recurrent network by implementing an epsilongreedy
policy within the GRU gate. An update gate in the GRU
determines whether to choose the random action (current input
of the GRU cell) or the optimal action (information from the
preceding GRU cell, containing actions with maximum rewards).
Additionally, it carries " and action values through each iteration,
assessing over-fitting by checking for duplicated predicted actions
in specific i terations. Subsequently, the " parameter in the agent
is reset. The results demonstrate that our proposed Policy GRURL
surpasses baseline approaches in terms of accuracy.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2024
Modified:
2025-06-06
Issued:
2025-06-06
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Thailand Section (IEEE Computer Society Thailand Chapter) and Prince of Songkla University. College of Computing. The 21st International Joint Conference on Computer Science and Software Engineering (JCSSE 2024)) (pp.551-557). Phuket : Prince of Songkla University
eng
©copyrights King Mongkut's University of Technology North Bangkok
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0.025135 วินาที
Chanapa Chanarong
| Title | Contributor | Type |
|---|---|---|
| Policy GRU-RL : simplified music playlist recommendation using sequential on reinforcement learning concept
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chanapa Chanarong;Saranya Maneeroj | บทความ/Article |
Saranya Maneeroj
| Title | Contributor | Type |
|---|---|---|
| Policy GRU-RL : simplified music playlist recommendation using sequential on reinforcement learning concept
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Chanapa Chanarong;Saranya Maneeroj | บทความ/Article |