Survey of Reinforcement Learning Based Recommender Systems
Recommender systems are devoted to find and automatically recommend valuable information and services for users from massive data,which can effectively solve the information overload problem,and become an important information technology in the era of big data.However,the problems of data sparsity,c...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2021-10-01
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| Series: | Jisuanji kexue |
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| Online Access: | http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-10-1.pdf |
| _version_ | 1818890969035046912 |
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| author | YU Li, DU Qi-han, YUE Bo-yan, XIANG Jun-yao, XU Guan-yu, LENG You-fang |
| author_facet | YU Li, DU Qi-han, YUE Bo-yan, XIANG Jun-yao, XU Guan-yu, LENG You-fang |
| author_sort | YU Li, DU Qi-han, YUE Bo-yan, XIANG Jun-yao, XU Guan-yu, LENG You-fang |
| collection | DOAJ |
| description | Recommender systems are devoted to find and automatically recommend valuable information and services for users from massive data,which can effectively solve the information overload problem,and become an important information technology in the era of big data.However,the problems of data sparsity,cold start,and interpretability are still the key technical difficulties that limit the wide application of the recommender systems.Reinforcement learning is an interactive learning technique,which can dynamically model user preferences by interacting with users and obtaining feedback to capture their interest drift in real time,and can better solve the classical key issues faced by traditional recommender systems.Nowadays,reinforcement lear-ning has become a hot research topic in the field of recommendation systems.From the perspective of survey,this paper first analyzes the improvement ideas of reinforcement learning for recommender systems based on a brief review of recommender systems and reinforcement learning.Then,the paper makes a general overview and summary of reinforcement learning based recommender systems in recent years,and further summarizes the research situation of traditional reinforcement learning based recommendation and deep reinforcement learning based recommendation respectively.Furthermore,the paper summarizes the frontiers of reinforcement learning based recommendation research topic in recent years and its application.Finally,the future development trend and application of reinforcement learning in recommender systems are analyzed.<br/> |
| first_indexed | 2024-12-19T17:33:21Z |
| format | Article |
| id | doaj.art-4960a44280504cf2baa0d863d016038b |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-12-19T17:33:21Z |
| publishDate | 2021-10-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-4960a44280504cf2baa0d863d016038b2022-12-21T20:12:24ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2021-10-01481011810.11896/jsjkx.210200085Survey of Reinforcement Learning Based Recommender SystemsYU Li, DU Qi-han, YUE Bo-yan, XIANG Jun-yao, XU Guan-yu, LENG You-fang01 School of Information,Renmin University of China,Beijing 100872,China<br/>2 XUTELI School,Beijing Institute of Technology,Beijing 100081,ChinaRecommender systems are devoted to find and automatically recommend valuable information and services for users from massive data,which can effectively solve the information overload problem,and become an important information technology in the era of big data.However,the problems of data sparsity,cold start,and interpretability are still the key technical difficulties that limit the wide application of the recommender systems.Reinforcement learning is an interactive learning technique,which can dynamically model user preferences by interacting with users and obtaining feedback to capture their interest drift in real time,and can better solve the classical key issues faced by traditional recommender systems.Nowadays,reinforcement lear-ning has become a hot research topic in the field of recommendation systems.From the perspective of survey,this paper first analyzes the improvement ideas of reinforcement learning for recommender systems based on a brief review of recommender systems and reinforcement learning.Then,the paper makes a general overview and summary of reinforcement learning based recommender systems in recent years,and further summarizes the research situation of traditional reinforcement learning based recommendation and deep reinforcement learning based recommendation respectively.Furthermore,the paper summarizes the frontiers of reinforcement learning based recommendation research topic in recent years and its application.Finally,the future development trend and application of reinforcement learning in recommender systems are analyzed.<br/>http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-10-1.pdfrecommender systems|reinforcement learning|deep reinforcement learning|markov decision process|multiple arm bandits |
| spellingShingle | YU Li, DU Qi-han, YUE Bo-yan, XIANG Jun-yao, XU Guan-yu, LENG You-fang Survey of Reinforcement Learning Based Recommender Systems Jisuanji kexue recommender systems|reinforcement learning|deep reinforcement learning|markov decision process|multiple arm bandits |
| title | Survey of Reinforcement Learning Based Recommender Systems |
| title_full | Survey of Reinforcement Learning Based Recommender Systems |
| title_fullStr | Survey of Reinforcement Learning Based Recommender Systems |
| title_full_unstemmed | Survey of Reinforcement Learning Based Recommender Systems |
| title_short | Survey of Reinforcement Learning Based Recommender Systems |
| title_sort | survey of reinforcement learning based recommender systems |
| topic | recommender systems|reinforcement learning|deep reinforcement learning|markov decision process|multiple arm bandits |
| url | http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-10-1.pdf |
| work_keys_str_mv | AT yuliduqihanyueboyanxiangjunyaoxuguanyulengyoufang surveyofreinforcementlearningbasedrecommendersystems |