{"type":"ScholarlyArticle","title":"Hierarchical Reinforcement Learning for Point of Interest Recommendation","authors":[{"name":"Yanan Xiao","slug":"yanan-xiao","given":"Yanan","family":"Xiao","affiliation":"School of Computer Science and Information Technology , Northeast Normal University , China; Key Laboratory of Applied Statistics of MOE , Northeast Normal University , China"},{"name":"Lu Jiang","slug":"lu-jiang","given":"Lu","family":"Jiang","affiliation":"Department of Information Science and Technology , Dalian Maritime University , China"},{"name":"Kunpeng Liu","slug":"kunpeng-liu","given":"Kunpeng","family":"Liu","affiliation":"Department of Computer Science , Portland State University , USA"},{"name":"Yuanbo Xu","slug":"yuanbo-xu","given":"Yuanbo","family":"Xu","affiliation":"College of Computer Science and Technology , Jilin University , China; Mobile Intelligent Computing (MIC) Lab , Jilin University , China"},{"name":"Pengyang Wang","slug":"pengyang-wang","given":"Pengyang","family":"Wang","affiliation":"Department of Computer and Information Science , University of Macau , China; The State Key Laboratory of Internet of Things for Smart City , University of Macau , China"},{"name":"Minghao Yin","slug":"minghao-yin","given":"Minghao","family":"Yin","affiliation":"School of Computer Science and Information Technology , Northeast Normal University , China; Key Laboratory of Applied Statistics of MOE , Northeast Normal University , China"}],"abstract":"With the increasing popularity of location-based services, accurately recommending points of interest (POIs) has become a critical task. Although existing technologies are proficient in processing time-series data, they fall short when it comes to accommodating the diversity and dynamism in users' POI selections, particularly in extracting key signals from complex historical behaviors. \r\nTo address this challenge, we introduced the Hierarchical Reinforcement Learning Preprocessing Framework (HRL-PRP), a framework that can be integrated into existing recommendation models to effectively optimize user profiles. \r\nThe HRL-PRP framework employs a two-tiered decision-making process, where the high-level process determines the necessity of modifying profiles, and the low-level process focuses on selecting POIs within the profiles. \r\nThrough evaluations on multiple real-world datasets, we have demonstrated that HRL-PRP surpasses existing state-of-the-art methods in various recommendation performance metrics.","doi":"10.24963/ijcai.2024/272","url":"https://ijcai.org/proceedings/2024/272","pdf":"https://ijcai.org/proceedings/2024/0272.pdf","markdown":"https://ijcai.org/proceedings/2024/272.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/272","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":272,"pages":{"first":2460,"last":2468},"keywords":["Data Mining: DM: Recommender systems"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2021] Yi-Chung Chen, Hsi-Ho Huang, Sheng- Min Chiu, and Chiang Lee. Joint promotion partner recommendation systems using data from location-based social networks. ISPRS International Journal of Geo- Information, 10(2):57, 2021.","title":"Joint promotion partner recommendation systems using data from location-based social networks","authors":"Yi-Chung Chen, Hsi-Ho Huang, Sheng-Min Chiu, Chiang Lee","year":"2021","venue":"ISPRS International Journal of Geo-Information","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Doan et al., 2019] Khoa D Doan, Guolei Yang, and Chan- dan K Reddy. An attentive spatio-temporal neural model for successive point of interest recommendation. In PAKDD, pages 346-358, 2019.","title":"An attentive spatio-temporal neural model for successive point of interest recommendation","authors":"Guolei Khoa D Doan, Chandan K Yang, Reddy","year":"2019","venue":"PAKDD","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Du et al., 2022] Qihan Du, Li Yu, Huiyuan Li, Youfang Leng, and Ningrui Ou. Denoising-oriented deep hierar- chical reinforcement learning for next-basket recommen- dation. In ICASSP, pages 4093-4097, 2022.","title":"Denoising-oriented deep hierarchical reinforcement learning for next-basket recommendation","authors":"Du","year":"2022","venue":"ICASSP","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Feng et al., 2018] Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. Deep- move: Predicting human mobility with attentional recur- rent networks. In WWW, pages 1459-1468, 2018.","title":"Deepmove: Predicting human mobility with attentional recurrent networks","authors":"Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, Depeng Jin","year":"2018","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Feng et al., 2020] Shanshan Feng, Lucas Vinh Tran, Gao Cong, Lisi Chen, Jing Li, and Fan Li. Hme: A hyperbolic metric embedding approach for next-poi recommendation. In SIGIR, pages 1429-1438, 2020.","title":"Hme: A hyperbolic metric embedding approach for next-poi recommendation","authors":"Shanshan Feng, Lucas Vinh Tran, Gao Cong, Lisi Chen, Jing Li, Fan Li","year":"2020","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[He et al., 2018] Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. Nais: Neural attentive item similarity model for recommenda- tion. IEEE Transactions on Knowledge and Data Engi- neering, 30(12):2354-2366, 2018.","title":"Nais: Neural attentive item similarity model for recommendation","authors":"He","year":"2018","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hu et al., 2023] Xuanming Hu, Wei Fan, Kun Yi, Pengfei Wang, Yuanbo Xu, Yanjie Fu, and Pengyang Wang. Boost- ing urban prediction via addressing spatial-temporal dis- tribution shift. In 2023 IEEE International Conference on Data Mining (ICDM), pages 160-169. IEEE, 2023.","title":"Boosting urban prediction via addressing spatial-temporal distribution shift","authors":"Xuanming Hu, Wei Fan, Kun Yi, Pengfei Wang, Yuanbo Xu, Yanjie Fu, Pengyang Wang","year":"2023","venue":"IEEE International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Huang et al., 2019] Liwei Huang, Yutao Ma, Shibo Wang, and Yanbo Liu. An attention-based spatiotemporal lstm network for next poi recommendation. IEEE Transactions on Services Computing, 14(6):1585-1597, 2019.","title":"An attention-based spatiotemporal lstm network for next poi recommendation","authors":"Liwei Huang, Yutao Ma, Shibo Wang, Yanbo Liu","year":"2019","venue":"IEEE Transactions on Services Computing","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Huang et al., 2020] Zhenhua Huang, Xiaolong Lin, Hai Liu, Bo Zhang, Yunwen Chen, and Yong Tang. Deep representation learning for location-based recommenda- tion. IEEE Transactions on Computational Social Systems, 7(3):648-658, 2020.","title":"Deep representation learning for location-based recommendation","authors":"Zhenhua Huang, Xiaolong Lin, Hai Liu, Bo Zhang, Yunwen Chen, Yong Tang","year":"2020","venue":"IEEE Transactions on Computational Social Systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jiang et al., 2023] Lu Jiang, Kunpeng Liu, Yibin Wang, Dongjie Wang, Pengyang Wang, Yanjie Fu, and Minghao Yin. Reinforced explainable knowledge concept recom- mendation in moocs. ACM Transactions on Intelligent Systems and Technology, pages 1-20, 2023.","title":"Reinforced explainable knowledge concept recommendation in moocs","authors":"Lu Jiang, Kunpeng Liu, Yibin Wang, Dongjie Wang, Pengyang Wang, Yanjie Fu, Minghao Yin","year":"2023","venue":"ACM Transactions on Intelligent Systems and Technology","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kong and Wu, 2018] Dejiang Kong and Fei Wu. A hierar- chical spatial-temporal long-short term memory network for location prediction. In IJCAI, volume 18, 2018.","title":"A hierarchical spatial-temporal long-short term memory network for location prediction","authors":"Dejiang Kong, Fei Wu","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kuanr and Mohanty, 2020] Madhusree Kuanr and Sachi Nandan Mohanty. Location-based person- alised recommendation systems for the tourists in india. International Journal of Business Intelligence and Data Mining, 17(3):377-392, 2020.","title":"Madhusree Kuanr and Sachi Nandan Mohanty. Location-based personalised recommendation systems for the tourists in india","authors":"Mohanty Kuanr","year":"2020","venue":"International Journal of Business Intelligence and Data Mining","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2017] Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. Neural attentive session-based recommendation. In CIKM, pages 1419- 1428, 2017.","title":"Neural attentive session-based recommendation","authors":"Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, Jun Ma","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Lian et al., 2020] Defu Lian, Yongji Wu, Yong Ge, Xing Xie, and Enhong Chen. Geography-aware sequential loca- tion recommendation. In KDD, pages 2009-2019, 2020.","title":"Geography-aware sequential location recommendation","authors":"Defu Lian, Yongji Wu, Yong Ge, Xing Xie, Enhong Chen","year":"2020","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2018] K Liu, P Wang, J Zhang, G Liu, Yan- jie Fu, and Sajal K Das. Incorporating interaction cou- pling among multi-view spatiotemporal contexts for mo- bike destination prediction. 2018.","title":"Incorporating interaction coupling among multi-view spatiotemporal contexts for mobike destination prediction","authors":"P Liu, J Wang, G Zhang, Yanjie Liu, Fu, Das","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu et al., 2020] Tongcun Liu, Jianxin Liao, Zhigen Wu, Yulong Wang, and Jingyu Wang. Exploiting geographical- temporal awareness attention for next point-of-interest rec- ommendation. Neurocomputing, 400:227-237, 2020.","title":"Exploiting geographicaltemporal awareness attention for next point-of-interest recommendation","authors":"Liu","year":"2020","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Liu, 2022] Xiaoqiang Liu. Poi recommendation model us- ing multi-head attention in location-based social network big data. International Journal of Information Technolo- gies and Systems Approach (IJITSA), 16(2):1-16, 2022.","title":"Poi recommendation model using multi-head attention in location-based social network big data","authors":"Xiaoqiang Liu","year":"2022","venue":"International Journal of Information Technologies and Systems Approach (IJITSA)","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Luo et al., 2021] Yingtao Luo, Qiang Liu, and Zhaocheng Liu. Stan: Spatio-temporal attention network for next lo- cation recommendation. In The Web Conference, pages 2177-2185, 2021.","title":"Stan: Spatio-temporal attention network for next location recommendation","authors":"Luo","year":"2021","venue":"The Web Conference","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Manotumruksa et al., 2020] Jarana Manotumruksa, Craig Macdonald, and Iadh Ounis. A contextual recurrent col- laborative filtering framework for modelling sequences of venue checkins. Information Processing & Management, 57(6):102092, 2020.","title":"A contextual recurrent collaborative filtering framework for modelling sequences of venue checkins","authors":"Jarana Manotumruksa, Craig Macdonald, Iadh Ounis","year":"2020","venue":"Information Processing & Management","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Memory, 2010] Long Short-Term Memory. Long short- term memory. Neural Computation, 9(8):1735-1780, 2010.","title":"Long Short-Term Memory. Long shortterm memory","authors":"Memory","year":"2010","venue":"Neural Computation","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Mnih et al., 2015] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Belle- mare, Alex Graves, Martin Riedmiller, Andreas K Fidje- land, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, pages 529-533, 2015.","title":"Human-level control through deep reinforcement learning","authors":"Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Parr and Russell, 1997] Ronald Parr and Stuart Russell. Re- inforcement learning with hierarchies of machines. Ad- vances in Neural Information Processing Systems, 10, 1997.","title":"Reinforcement learning with hierarchies of machines","authors":"Russell ; Parr, Ronald Parr, Stuart Russell","year":"1997","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Qin et al., 2023] Yifang Qin, Yifan Wang, Fang Sun, Wei Ju, Xuyang Hou, Zhe Wang, Jia Cheng, Jun Lei, and Ming Zhang. Disenpoi: Disentangling sequential and geograph- ical influence for point-of-interest recommendation. In WSDM, pages 508-516, 2023.","title":"Disenpoi: Disentangling sequential and geographical influence for point-of-interest recommendation","authors":"Yifang Qin, Yifan Wang, Fang Sun, Wei Ju, Xuyang Hou, Zhe Wang, Jia Cheng, Jun Lei, Ming Zhang","year":"2023","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Rendle et al., 2010] Steffen Rendle, Christoph Freuden- thaler, and Lars Schmidt-Thieme. Factorizing personal- ized markov chains for next-basket recommendation. In WWW, pages 811-820, 2010.","title":"Factorizing personalized markov chains for next-basket recommendation","authors":"Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme","year":"2010","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Sanz-Cruzado et al., 2019] Javier Sanz-Cruzado, Pablo Castells, and Esther López. A simple multi-armed nearest-neighbor bandit for interactive recommendation. In RecSys, pages 358-362, 2019.","title":"A simple multi-armed nearest-neighbor bandit for interactive recommendation","authors":"Javier Sanz-Cruzado, Pablo Castells, Esther López","year":"2019","venue":"RecSys","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Sun et al., 2020] Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, and Hongzhi Yin. Where to go next: Modeling long-and short-term user prefer- ences for point-of-interest recommendation. In AAAI, vol- ume 34, pages 214-221, 2020.","title":"Where to go next: Modeling long-and short-term user preferences for point-of-interest recommendation","authors":"Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet, Hung Nguyen, Hongzhi Yin","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Sutton and Barto, 2018] Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.","title":"Reinforcement learning: An introduction","authors":"S Richard, Andrew G Sutton, Barto","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Thomas and Brunskill, 2017] Philip S Thomas and Emma Brunskill. Policy gradient methods for reinforce- ment learning with function approximation and action- dependent baselines. arXiv preprint arXiv:1706.06643, 2017.","title":"Policy gradient methods for reinforcement learning with function approximation and actiondependent baselines","authors":"S Philip, Emma Thomas, Brunskill","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2018] Pengyang Wang, Yanjie Fu, Jiawei Zhang, Pengfei Wang, Yu Zheng, and Charu Aggarwal. You are how you drive: Peer and temporal-aware repre- sentation learning for driving behavior analysis. In Pro- ceedings of the 24th ACM SIGKDD International Con- ference on Knowledge Discovery & Data Mining, pages 2457-2466, 2018.","title":"You are how you drive: Peer and temporal-aware representation learning for driving behavior analysis","authors":"Wang","year":"2018","venue":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2019a] Pengyang Wang, Yanjie Fu, Hui Xiong, and Xiaolin Li. Adversarial substructured representation learning for mobile user profiling. In Proceedings of the 25th ACM SIGKDD International Conference on Knowl- edge Discovery & Data Mining, pages 130-138, 2019.","title":"Adversarial substructured representation learning for mobile user profiling","authors":"Wang","year":"2019","venue":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2019b] Pengyang Wang, Xiaolin Li, Yu Zheng, Charu Aggarwal, and Yanjie Fu. Spatiotemporal represen- tation learning for driving behavior analysis: A joint per- spective of peer and temporal dependencies. IEEE Trans- actions on Knowledge and Data Engineering, 33(2):728- 741, 2019.","title":"Spatiotemporal representation learning for driving behavior analysis: A joint perspective of peer and temporal dependencies","authors":"Wang","year":"2019","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wang et al., 2020] Pengyang Wang, Kunpeng Liu, Lu Jiang, Xiaolin Li, and Yanjie Fu. Incremental mobile user profiling: Reinforcement learning with spatial knowledge graph for modeling event streams. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 853-861, 2020.","title":"Incremental mobile user profiling: Reinforcement learning with spatial knowledge graph for modeling event streams","authors":"Wang","year":"2020","venue":"Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wang et al., 2022] Enshu Wang, Rong Ding, Zhaoxing Yang, Haiming Jin, Chenglin Miao, Lu Su, Fan Zhang, Chunming Qiao, and Xinbing Wang. Joint charging and relocation recommendation for e-taxi drivers via multi-agent mean field hierarchical reinforcement learn- ing. IEEE Transactions on Mobile Computing, pages 1274-1290, 2022.","title":"Joint charging and relocation recommendation for e-taxi drivers via multi-agent mean field hierarchical reinforcement learning","authors":"Enshu Wang, Rong Ding, Zhaoxing Yang, Haiming Jin, Chenglin Miao, Lu Su, Fan Zhang, Chunming Qiao, Xinbing Wang","year":"2022","venue":"IEEE Transactions on Mobile Computing","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang et al., 2023a] Dongjie Wang, Pengyang Wang, Yanjie Fu, Kunpeng Liu, Hui Xiong, and Charles E Hughes. Re- inforced imitative graph learning for mobile user profiling. IEEE Transactions on Knowledge and Data Engineering, 2023.","title":"Reinforced imitative graph learning for mobile user profiling","authors":"Wang","year":"2023","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wang et al., 2023b] Yu Wang, An Liu, and Fan Jiang. A category-aware network for next poi recommendation with long-and short-term preferences. IEEE Transactions on Computational Social Systems, 2023.","title":"A category-aware network for next poi recommendation with long-and short-term preferences","authors":"Wang","year":"2023","venue":"IEEE Transactions on Computational Social Systems","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wu et al., 2019] Yuxia Wu, Ke Li, Guoshuai Zhao, and Xueming Qian. Long-and short-term preference learning for next poi recommendation. In CIKM, pages 2301-2304, 2019.","title":"Long-and short-term preference learning for next poi recommendation","authors":"Yuxia Wu, Ke Li, Guoshuai Zhao, Xueming Qian","year":"2019","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Xie et al., 2021] Ruobing Xie, Shaoliang Zhang, Rui Wang, Feng Xia, and Leyu Lin. Hierarchical reinforcement learn- ing for integrated recommendation. In AAAI, pages 4521- 4528, 2021.","title":"Hierarchical reinforcement learning for integrated recommendation","authors":"Xie","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Xu et al., 2017] Yi-Ning Xu, Lei Xu, Ling Huang, and Chang-Dong Wang. Social and content based collabo- rative filtering for point-of-interest recommendations. In ICONIP, pages 46-56. Springer, 2017.","title":"Social and content based collaborative filtering for point-of-interest recommendations","authors":"Yi-Ning Xu, Lei Xu, Ling Huang, Chang-Dong Wang","year":"2017","venue":"ICONIP","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Xu et al., 2022] Hengpeng Xu, Wenjian Ding, Wei Shen, Jun Wang, and Zhenglu Yang. Deep convolutional re- current model for region recommendation with spatial and temporal contexts. Ad Hoc Networks, 129:102545, 2022.","title":"Deep convolutional recurrent model for region recommendation with spatial and temporal contexts","authors":"Xu","year":"2022","venue":"Ad Hoc Networks","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Yao et al., 2017] Di Yao, Chao Zhang, Jianhui Huang, and Jingping Bi. Serm: A recurrent model for next location prediction in semantic trajectories. In CIKM, pages 2411- 2414, 2017.","title":"Serm: A recurrent model for next location prediction in semantic trajectories","authors":"Di Yao, Chao Zhang, Jianhui Huang, Jingping Bi","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yin et al., 2021] Minghao Yin, Yanheng Liu, Xu Zhou, and Geng Sun. A tensor decomposition based collaborative filtering algorithm for time-aware poi recommendation in lbsn. Multimedia Tools and Applications, 80(30):36215- 36235, 2021.","title":"A tensor decomposition based collaborative filtering algorithm for time-aware poi recommendation in lbsn","authors":"Minghao Yin, Yanheng Liu, Xu Zhou, Geng Sun","year":"2021","venue":"Multimedia Tools and Applications","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yu et al., 2020] Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Jie Tang, Minlie Huang, and Zhiyuan Liu. Ex- panrl: Hierarchical reinforcement learning for course con- cept expansion in moocs. In AACL-IJCNLP, pages 770- 780, 2020.","title":"Expanrl: Hierarchical reinforcement learning for course concept expansion in moocs","authors":"Jifan Yu, Chenyu Wang, Gan Luo, Lei Hou, Juanzi Li, Jie Tang, Minlie Huang, Zhiyuan Liu","year":"2020","venue":"AACL-IJCNLP","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang et al., 2019] Jing Zhang, Bowen Hao, Bo Chen, Cuiping Li, Hong Chen, and Jimeng Sun. Hierarchi- cal reinforcement learning for course recommendation in moocs. In AAAI, pages 435-442, 2019.","title":"Hierarchical reinforcement learning for course recommendation in moocs","authors":"Jing Zhang, Bowen Hao, Bo Chen, Cuiping Li, Hong Chen, Jimeng Sun","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2024] Zhaofan Zhang, Yanan Xiao, Lu Jiang, Dingqi Yang, Minghao Yin, and Pengyang Wang. Spatial- temporal interplay in human mobility: A hierarchical re- inforcement learning approach with hypergraph represen- tation. pages 9396-9404. AAAI Press, 2024.","title":"Spatialtemporal interplay in human mobility: A hierarchical reinforcement learning approach with hypergraph representation","authors":"Zhang","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}