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Paper Abstract and Keywords
Presentation 2026-06-12 11:25
Balancing Recommendation Precision and Cache Hit Rate Using GNN and Reinforcement Learning in Dynamic Environments
Shota Ochiai, Noriaki Kamiyama (Ritsumeikan Univ.) NLP2026-19 CCS2026-19
Abstract (in Japanese) (See Japanese page) 
(in English) While content recommendation systems and Content Delivery Networks (CDNs) are essential for modern services, their desired recommendation targets often conflict. Existing methods combining collaborative filtering (CF) and reinforcement learning (RL) struggle to capture complex user-item relationships in dynamic environments, limiting accuracy. This paper proposes a novel method that integrates Graph Neural Networks (GNNs) to improve recommendation accuracy, alongside an RL agent that utilizes GNN prediction scores as state information to optimize cache hit rates. Simulations confirm our approach significantly outperforms conventional CF-based methods in both recommendation accuracy and cache efficiency.
Keyword (in Japanese) (See Japanese page) 
(in English) GNN / Reinforcement learning / CDN / Recommendation accuracy / Cache hit ratio / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 69, CCS2026-19, pp. 99-104, June 2026.
Paper # CCS2026-19 
Date of Issue 2026-06-04 (NLP, CCS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NLP2026-19 CCS2026-19

Conference Information
Committee CCS NLP  
Conference Date 2026-06-11 - 2026-06-12 
Place (in Japanese) (See Japanese page) 
Place (in English) I-site Namba 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear Problems, Complex Communication Sciences, etc. 
Paper Information
Registration To CCS 
Conference Code 2026-06-CCS-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Balancing Recommendation Precision and Cache Hit Rate Using GNN and Reinforcement Learning in Dynamic Environments 
Sub Title (in English)  
Keyword(1) GNN  
Keyword(2) Reinforcement learning  
Keyword(3) CDN  
Keyword(4) Recommendation accuracy  
Keyword(5) Cache hit ratio  
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Keyword(7)  
Keyword(8)  
1st Author's Name Shota Ochiai  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Noriaki Kamiyama  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker Author-1 
Date Time 2026-06-12 11:25:00 
Presentation Time 25 minutes 
Registration for CCS 
Paper # NLP2026-19, CCS2026-19 
Volume (vol) vol.126 
Number (no) no.68(NLP), no.69(CCS) 
Page pp.99-104 
#Pages 6 
Date of Issue 2026-06-04 (NLP, CCS) 

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