Acknowledgement
This research was supported by the National Research Foundation of Korea (No. NRF-2022R1A2C1005921) and BK21 FOUR (Fostering Outstanding Universities for Research) (No.5199990914048)
Proceedings of the Korea Information Processing Society Conference (한국정보처리학회:학술대회논문집)
- 2023.11a
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- Pages.406-409
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- 2023
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- 2005-0011(pISSN)
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- 2671-7298(eISSN)
DOI QR Code
Implementation of a Recommendation system using the advanced deep reinforcement learning method
고급 심층 강화학습 기법을 이용한 추천 시스템 구현
- Sony Peng (Dept. of Software Convergence, SoonChunHyang University) ;
- Sophort Siet (Dept. of Software Convergence, SoonChunHyang University) ;
- Sadriddinov Ilkhomjon (Dept. of Software Convergence, SoonChunHyang University) ;
- DaeYoung, Kim (Dept. of Software Convergence, SoonChunHyang University) ;
- Doo-Soon Park (Dept. of Software Convergence, SoonChunHyang University)
- 펭소니 (순천향대학교 소프트웨어융합학과) ;
- 싯소포호트 (순천향대학교 소프트웨어융합학과) ;
- 일홈존 (순천향대학교 소프트웨어융합학과) ;
- 김대영 (순천향대학교 소프트웨어융합학과) ;
- 박두순 (순천향대학교 소프트웨어융합학과)
- Published : 2023.11.02
Abstract
With the explosion of information, recommendation algorithms are becoming increasingly important in providing people with appropriate content, enhancing their online experience. In this paper, we propose a recommender system using advanced deep reinforcement learning(DRL) techniques. This method is more adaptive and integrative than traditional methods. We selected the MovieLens dataset and employed the precision metric to assess the effectiveness of our algorithm. The result of our implementation outperforms other baseline techniques, delivering better results for Top-N item recommendations.
Keywords