Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration
In recent years,the application of deep reinforcement learning in recommendation system has attracted much attention.Based on the existing research,this paper proposes a new recommendation model RP-Dueling,which is based on the deep reinforcement learning Dueling-DQN algorithm,and adds the regret ex...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2022-06-01
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| Series: | Jisuanji kexue |
| Subjects: | |
| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-6-149.pdf |
| _version_ | 1827965578109779968 |
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| author | HONG Zhi-li, LAI Jun, CAO Lei, CHEN Xi-liang, XU Zhi-xiong |
| author_facet | HONG Zhi-li, LAI Jun, CAO Lei, CHEN Xi-liang, XU Zhi-xiong |
| author_sort | HONG Zhi-li, LAI Jun, CAO Lei, CHEN Xi-liang, XU Zhi-xiong |
| collection | DOAJ |
| description | In recent years,the application of deep reinforcement learning in recommendation system has attracted much attention.Based on the existing research,this paper proposes a new recommendation model RP-Dueling,which is based on the deep reinforcement learning Dueling-DQN algorithm,and adds the regret exploration mechanism to make the algorithm adaptively and dynamically adjust the proportion of “exploration-utilization” according to the training degree.The algorithm can capture users’ dynamic interest and fully explore the action space in the recommendation system with large-scale state space.By testing the proposed algorithm model on multiple data sets,the optimal average results of <i>MAE </i>and <i>RMSE </i>are 0.16 and 0.43 respectively,which are 0.48 and 0.56 higher than the current optimal research results.Experimental results show that the proposed model is superior to the existing traditional recommendation model and recommendation model based on deep reinforcement learning. |
| first_indexed | 2024-04-09T17:35:27Z |
| format | Article |
| id | doaj.art-e3d8db531ff64787bb92f11f7df810a7 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-04-09T17:35:27Z |
| publishDate | 2022-06-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-e3d8db531ff64787bb92f11f7df810a72023-04-18T02:32:00ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2022-06-0149614915710.11896/jsjkx.210600226Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret ExplorationHONG Zhi-li, LAI Jun, CAO Lei, CHEN Xi-liang, XU Zhi-xiong0Command & Control Engineering College,Army Engineering University of PLA,Nanjing 210007,ChinaIn recent years,the application of deep reinforcement learning in recommendation system has attracted much attention.Based on the existing research,this paper proposes a new recommendation model RP-Dueling,which is based on the deep reinforcement learning Dueling-DQN algorithm,and adds the regret exploration mechanism to make the algorithm adaptively and dynamically adjust the proportion of “exploration-utilization” according to the training degree.The algorithm can capture users’ dynamic interest and fully explore the action space in the recommendation system with large-scale state space.By testing the proposed algorithm model on multiple data sets,the optimal average results of <i>MAE </i>and <i>RMSE </i>are 0.16 and 0.43 respectively,which are 0.48 and 0.56 higher than the current optimal research results.Experimental results show that the proposed model is superior to the existing traditional recommendation model and recommendation model based on deep reinforcement learning.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-6-149.pdfrecommendation system|deep reinforcement learning|dueling-dqn|rp-dueling|dynamic interest|regret exploration |
| spellingShingle | HONG Zhi-li, LAI Jun, CAO Lei, CHEN Xi-liang, XU Zhi-xiong Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration Jisuanji kexue recommendation system|deep reinforcement learning|dueling-dqn|rp-dueling|dynamic interest|regret exploration |
| title | Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration |
| title_full | Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration |
| title_fullStr | Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration |
| title_full_unstemmed | Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration |
| title_short | Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration |
| title_sort | study on intelligent recommendation method of dueling network reinforcement learning based on regret exploration |
| topic | recommendation system|deep reinforcement learning|dueling-dqn|rp-dueling|dynamic interest|regret exploration |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-6-149.pdf |
| work_keys_str_mv | AT hongzhililaijuncaoleichenxiliangxuzhixiong studyonintelligentrecommendationmethodofduelingnetworkreinforcementlearningbasedonregretexploration |