第3期 柴瑞敏，等：基于时空循环神经网络的下一个兴趣点推荐方法 ·415· [14]HOCHREITER S,SCHMIDHUBER J.Long short-term visitors[C]//Proceedings of the Thirty-First AAAI Confer- memory[J].Neural computation,1997,9(8):1735-1780. ence on Artificial Intelligence.San Francisco,USA,2017: [15]CHUNG J,GULCEHRE C,CHO K,et al.Gated feed- 102-108. back recurrent neural networks[C]//Proceedings of the [25]CUI Qiang,TANG Yuyuan,WU Shu,et al.Distance2Pre: 32nd International Conference on Machine Learning. personalized spatial preference for next point-of-interest Lille,France,.2015:2067-2075. prediction[C]//23rd Pacific-Asia Conference on Ad- [16]孟祥福，齐雪月，张全贵，等.基于用户兴趣点耦合关系 vances in Knowledge Discovery and Data Mining.Ma- 的兴趣点推荐方法[.智能系统学报，2021,16(2)： cau,China,2019:289-301. 228-236. [26]ZHAO Pengpeng,ZHU Haifeng,LIU Yanchi,et al. MENG Xiangfu,QI Xueyue,ZHANG Quangui,et al.A Where to go next:a spatio-temporal gated network for POI recommendation approach based on user-poi coup- next POI recommendation[J].Proceedings of the AAAI ling relationships[J].CAAI transactions on intelligent sys- conference on artificial intelligence,2019,33(1): tems,2021,16(2):228-236. 5877-5884. [17]FENG Shanshan,LI Xutao,ZENG Yifeng,et al.Person- [27]YUAN Quan,CONG Gao,MA Zongyang,et al.Time- alized ranking metric embedding for next new POI re- aware point-of-interest recommendation[C]//Proceedings commendation[C]//Proceedings of the Twenty-Fourth In- of the 36th International ACM SIGIR Conference on Re- ternational Conference on Artificial Intelligence.Buenos search and Development in Information Retrieval. Aires,Argentina,2015:2069-2075. Montreal,Canada,2013:363-372. [18]XU Shuai,CAO Jiuxin,LEGG P,et al.Venue2Vec:an [28]HE Jing,LI Xin,LIAO Lejian.Next point-of-interest re- efficient embedding model for fine-grained user location commendation via a category-aware Listwise Bayesian prediction in geo-social networks[J].IEEE systems journ- Personalized Ranking[J].Journal of computational sci- al,2020,142):1740-1751. ence,2018,28:206-216. [19]SALAKHUTDINOV R,MNIH A.Probabilistic matrix [29]RENDLE S,FREUDENTHALER C,GANTNER Z,et al. factorization[C]//Proceedings of the 20th International BPR:bayesian personalized ranking from implicit feed- Conference on Neural Information Processing Systems. back[C]//Proceedings of the 25th Conference on Uncer- Vancouver,Canada,2007:1257-1264 tainty in Artificial Intelligence.Montreal,Canada,2009: [20]HE Xiannan,LIAO Lizi,ZHANG Hanwang,et al.Neur- 452-461 al collaborative filtering[C]//Proceedings of the 26th In- ternational Conference on World Wide Web.Perth,Aus- 作者简介： tralia2017:173-182. 柴瑞敏，副教授，主要研究方向为 [21]CHENG Chen,YANG Haiqin,LYU M R,et al.Where 数据库理论、数据挖掘。参与项目 you like to go next:successive point-of-interest recom- 10余项。参编教材3部，发表学术论 mendation[C]//Proceedings of the 23rd International Joint 文30余篇。 Conference on Artificial Intelligence.Beijing,China, 2013:2605-2611. [22]XIE Min,YIN Hongzhi,WANG Hao,et al.Learning graph-based POI embedding for location-based recom- 殷臣，硕士研究生，主要研究方向 mendation[Cl//Proceedings of the 25th ACM Internation- 为推荐系统、深度学习。 al on Conference on Information and Knowledge Man- agement.Indianapolis,USA,2016:15-24. [23]鲜学丰，陈晓杰，赵朋朋，等.基于上下文感知和个性化 度量嵌入的下一个兴趣点推荐[).计算机工程与科学， 2018.40(4:616-625. XIAN Xuefeng,CHEN Xiaojie,ZHAO Pengpeng,et al. 孟祥福，教授，博士，主要研究方 向为空间关键字查询、大数据分析与 Context-aware personalized metric embedding for next 可视化、机器学习、推荐系统。主持国 POI recommendation[J].Computer engineering sci- 家自然科学基金20项，获授权发明专 ence,2018,40(4):616-625. 利1项。出版专著1部，发表学术论 [24]FENG Shanshan,CONG Gao,AN Bo,et al.POI2Vec: 文50余篇。 geographical latent representation for predicting futureHOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural computation, 1997, 9(8): 1735–1780. [14] CHUNG J, GULCEHRE C, CHO K, et al. Gated feed￾back recurrent neural networks[C]//Proceedings of the 32nd International Conference on Machine Learning. Lille, France, 2015: 2067−2075. [15] 孟祥福, 齐雪月, 张全贵, 等. 基于用户-兴趣点耦合关系 的兴趣点推荐方法 [J]. 智能系统学报, 2021, 16(2): 228–236. MENG Xiangfu，QI Xueyue，ZHANG Quangui，et al. A POI recommendation approach based on user-poi coup￾ling relationships[J]. CAAI transactions on intelligent sys￾tems, 2021, 16(2): 228–236. [16] FENG Shanshan, LI Xutao, ZENG Yifeng, et al. Person￾alized ranking metric embedding for next new POI re￾commendation[C]//Proceedings of the Twenty-Fourth In￾ternational Conference on Artificial Intelligence. Buenos Aires, Argentina, 2015: 2069−2075. [17] XU Shuai, CAO Jiuxin, LEGG P, et al. Venue2Vec: an efficient embedding model for fine-grained user location prediction in geo-social networks[J]. IEEE systems journ￾al, 2020, 14(2): 1740–1751. [18] SALAKHUTDINOV R, MNIH A. Probabilistic matrix factorization[C]//Proceedings of the 20th International Conference on Neural Information Processing Systems. Vancouver, Canada, 2007: 1257−1264. [19] HE Xiannan, LIAO Lizi, ZHANG Hanwang, et al. Neur￾al collaborative filtering[C]//Proceedings of the 26th In￾ternational Conference on World Wide Web. Perth, Aus￾tralia, 2017: 173−182. [20] CHENG Chen, YANG Haiqin, LYU M R, et al. Where you like to go next: successive point-of-interest recom￾mendation[C]//Proceedings of the 23rd International Joint Conference on Artificial Intelligence. Beijing, China, 2013: 2605−2611. [21] XIE Min, YIN Hongzhi, WANG Hao, et al. Learning graph-based POI embedding for location-based recom￾mendation[C]//Proceedings of the 25th ACM Internation￾al on Conference on Information and Knowledge Man￾agement. Indianapolis, USA, 2016: 15−24. [22] 鲜学丰, 陈晓杰, 赵朋朋, 等. 基于上下文感知和个性化 度量嵌入的下一个兴趣点推荐 [J]. 计算机工程与科学, 2018, 40(4): 616–625. XIAN Xuefeng, CHEN Xiaojie, ZHAO Pengpeng, et al. Context-aware personalized metric embedding for next POI recommendation[J]. Computer engineering & sci￾ence, 2018, 40(4): 616–625. [23] FENG Shanshan, CONG Gao, AN Bo, et al. POI2Vec: geographical latent representation for predicting future [24] visitors[C]//Proceedings of the Thirty-First AAAI Confer￾ence on Artificial Intelligence. San Francisco, USA, 2017: 102−108. CUI Qiang, TANG Yuyuan, WU Shu, et al. Distance2Pre: personalized spatial preference for next point-of-interest prediction[C]//23rd Pacific-Asia Conference on Ad￾vances in Knowledge Discovery and Data Mining. Ma￾cau, China, 2019: 289−301. [25] ZHAO Pengpeng, ZHU Haifeng, LIU Yanchi, et al. Where to go next: a spatio-temporal gated network for next POI recommendation[J]. Proceedings of the AAAI conference on artificial intelligence, 2019, 33(1): 5877–5884. [26] YUAN Quan, CONG Gao, MA Zongyang, et al. Time￾aware point-of-interest recommendation[C]//Proceedings of the 36th International ACM SIGIR Conference on Re￾search and Development in Information Retrieval. Montreal, Canada, 2013: 363−372. [27] HE Jing, LI Xin, LIAO Lejian. Next point-of-interest re￾commendation via a category-aware Listwise Bayesian Personalized Ranking[J]. Journal of computational sci￾ence, 2018, 28: 206–216. [28] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: bayesian personalized ranking from implicit feed￾back[C]//Proceedings of the 25th Conference on Uncer￾tainty in Artificial Intelligence. Montreal, Canada, 2009: 452−461. [29] 作者简介： 柴瑞敏，副教授，主要研究方向为 数据库理论、数据挖掘。参与项目 10 余项。参编教材 3 部，发表学术论 文 30 余篇。 殷臣，硕士研究生，主要研究方向 为推荐系统、深度学习。 孟祥福，教授，博士，主要研究方 向为空间关键字查询、大数据分析与 可视化、机器学习、推荐系统。主持国 家自然科学基金 20 项，获授权发明专 利 1 项。出版专著 1 部，发表学术论 文 50 余篇。 第 3 期 柴瑞敏，等：基于时空循环神经网络的下一个兴趣点推荐方法 ·415·