{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:55:40Z","timestamp":1774122940457,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s13042-023-02093-0","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T13:02:53Z","timestamp":1708434173000},"page":"3299-3312","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Pedestrian trajectory prediction based on spatio-temporal attention mechanism"],"prefix":"10.1007","volume":"15","author":[{"given":"Jun","family":"Hu","sequence":"first","affiliation":[]},{"given":"Xinyu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Qinghua","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,20]]},"reference":[{"key":"2093_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109848","volume":"256","author":"J Fang","year":"2022","unstructured":"Fang J, Meng X (2022) URPI-GRU: an approach of next POI recommendation based on user relationship and preference information. Knowl-Based Syst 256:109848","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"2093_CR2","first-page":"1","volume":"16","author":"Z Wang","year":"2022","unstructured":"Wang Z, Zhu Y, Zhang Q, Liu H, Wang C, Liu T (2022) Graph-enhanced spatial\u2013temporal network for next POI recommendation. ACM Trans Knowl Discov Data 16(6):1\u201321","journal-title":"ACM Trans Knowl Discov Data"},{"key":"2093_CR3","doi-asserted-by":"publisher","unstructured":"Huang J, Tong Z, Feng Z (2022) Geographical POI recommendation for internet of things: a federated learning approach using matrix factorization. Int J Commun Syst. https:\/\/doi.org\/10.1002\/dac.5161","DOI":"10.1002\/dac.5161"},{"key":"2093_CR4","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.neucom.2021.09.056","volume":"467","author":"X Liu","year":"2022","unstructured":"Liu X, Yang Y, Xu Y, Yang F, Huang Q, Wang H (2022) Real-time POI recommendation via modeling long-and short-term user preferences. Neurocomputing 467:454\u2013464","journal-title":"Neurocomputing"},{"key":"2093_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126272","volume":"543","author":"F Mo","year":"2023","unstructured":"Mo F, Yamana H (2023) EPT-GCN: Edge propagation-based time-aware graph convolution network for POI recommendation. Neurocomputing 543:126272","journal-title":"Neurocomputing"},{"key":"2093_CR6","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.neucom.2022.10.048","volume":"518","author":"J Fang","year":"2023","unstructured":"Fang J, Meng X, Qi X (2023) A top-k POI recommendation approach based on LBSN and multi-graph fusion. Neurocomputing 518:219\u2013230","journal-title":"Neurocomputing"},{"key":"2093_CR7","unstructured":"Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: successive point-of-interest recommendation. In: Twenty-third international joint conference on artificial intelligence"},{"issue":"8","key":"2093_CR8","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"2093_CR9","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555"},{"key":"2093_CR10","doi-asserted-by":"crossref","unstructured":"Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: The AAAI Conference on Artificial Intelligence, pp 194\u2013200","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"2093_CR11","doi-asserted-by":"crossref","unstructured":"Zhu Y, Li H, Liao Y, Wang B, Guan Z, Liu H, Cai D (2017) What to do next: modeling user behaviors by time-LSTM. In: International joint conference on artificial intelligence, pp 3602\u20133608","DOI":"10.24963\/ijcai.2017\/504"},{"key":"2093_CR12","doi-asserted-by":"crossref","unstructured":"Manotumruksa J, Macdonald C, Ounis I (2018) A contextual attention recurrent architecture for context-aware venue recommendation. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 555\u2013564","DOI":"10.1145\/3209978.3210042"},{"key":"2093_CR13","doi-asserted-by":"crossref","unstructured":"Yao D, Zhang C, Huang J, Bi J (2017) SERM: A recurrent model for next location prediction in semantic trajectories. In: the 2017 ACM on conference on information and knowledge management, pp 2411\u20132414","DOI":"10.1145\/3132847.3133056"},{"key":"2093_CR14","doi-asserted-by":"crossref","unstructured":"Manotumruksa J, Macdonald C, Ounis I (2017) A deep recurrent collaborative filtering framework for venue recommendation. In: the 2017 ACM on conference on information and knowledge management, pp 1429\u20131438","DOI":"10.1145\/3132847.3133036"},{"key":"2093_CR15","doi-asserted-by":"crossref","unstructured":"Sun K, Qian T, Chen T, Liang Y, Nguyen QVH, Yin H (2020) Where to go next: modeling long-and short-term user preferences for point-of-interest recommendation. In: The AAAI conference on artificial intelligence, pp 214\u2013221","DOI":"10.1609\/aaai.v34i01.5353"},{"issue":"7","key":"2093_CR16","doi-asserted-by":"publisher","first-page":"3174","DOI":"10.1002\/int.22412","volume":"36","author":"Y Liu","year":"2021","unstructured":"Liu Y, Pei A, Wang F, Yang Y, Zhang X, Wang H, Dai H, Qi L, Ma R (2021) An attention-based category-aware GRU model for the next POI recommendation. Int J Intell Syst 36(7):3174\u20133189","journal-title":"Int J Intell Syst"},{"key":"2093_CR17","doi-asserted-by":"crossref","unstructured":"Wang M, Lu Y, Huang J (2019) Spent: a successive POI recommendation method using similarity-based POI embedding and recurrent neural network with temporal influence. In: 2019 IEEE international conference on big data and smart computing, pp 1\u20138","DOI":"10.1109\/BIGCOMP.2019.8679431"},{"key":"2093_CR18","doi-asserted-by":"crossref","unstructured":"Wang X, Liu X, Li L, Chen X, Liu J, Wu H (2021) Time-aware user modeling with check-in time prediction for next POI recommendation. In: 2021 IEEE international conference on web services, pp 125\u2013134","DOI":"10.1109\/ICWS53863.2021.00028"},{"key":"2093_CR19","doi-asserted-by":"crossref","unstructured":"Halder S, Lim KH, Chan J, Zhang X (2021) Transformer-based multi-task learning for queuing time aware next POI recommendation. In: Advances in knowledge discovery and data mining: 25th Pacific-Asia conference, Part II, pp 510\u2013523","DOI":"10.1007\/978-3-030-75765-6_41"},{"issue":"1","key":"2093_CR20","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1007\/s41019-022-00180-w","volume":"7","author":"S Dai","year":"2022","unstructured":"Dai S, Yu Y, Fan H, Dong J (2022) Spatio-temporal representation learning with social tie for personalized POI recommendation. Data Sci Eng 7(1):44\u201356","journal-title":"Data Sci Eng"},{"key":"2093_CR21","doi-asserted-by":"crossref","unstructured":"Luo Y, Liu Q, Liu Z (2021) STAN: spatio-temporal attention network for next location recommendation. In: Proceedings of the web conference, pp 2177\u20132185","DOI":"10.1145\/3442381.3449998"},{"issue":"5","key":"2093_CR22","doi-asserted-by":"publisher","first-page":"1749","DOI":"10.1007\/s11280-021-00895-2","volume":"24","author":"H Sun","year":"2021","unstructured":"Sun H, Xu J, Zhou R, Chen W, Zhao L, Liu C (2021) HOPE: a hybrid deep neural model for out-of-town next POI recommendation. World Wide Web 24(5):1749\u20131768","journal-title":"World Wide Web"},{"key":"2093_CR23","doi-asserted-by":"crossref","unstructured":"Yang D, Fankhauser B, Rosso P, Cudre-Mauroux P (2020) Location prediction over sparse user mobility traces using RNNs. In: The twenty-ninth international joint conference on artificial intelligence, pp 2184\u20132190","DOI":"10.24963\/ijcai.2020\/302"},{"issue":"3","key":"2093_CR24","doi-asserted-by":"publisher","first-page":"4075","DOI":"10.3233\/JIFS-200465","volume":"40","author":"M Shi","year":"2021","unstructured":"Shi M, Shen D, Kou Y, Nie T, Yu G (2021) Next point-of-interest recommendation by sequential feature mining and public preference awareness. J Intell Fuzzy Syst 40(3):4075\u20134090","journal-title":"J Intell Fuzzy Syst"},{"key":"2093_CR25","doi-asserted-by":"crossref","unstructured":"Feng J, Li Y, Zhang C, Sun F, Meng F, Guo A, Jin D (2018) DeepMove: Predicting human mobility with attentional recurrent networks. In: The 2018 world wide web conference, pp 1459\u20131468","DOI":"10.1145\/3178876.3186058"},{"key":"2093_CR26","doi-asserted-by":"crossref","unstructured":"Cui Q, Tang Y, Wu S, Wang L (2019) Distance2Pre: personalized spatial preference for next point-of-interest prediction. In: Advances in knowledge discovery and data mining: 23rd Pacific-Asia conference","DOI":"10.1007\/978-3-030-16142-2_23"},{"issue":"5","key":"2093_CR27","doi-asserted-by":"publisher","first-page":"2512","DOI":"10.1109\/TKDE.2020.3007194","volume":"34","author":"P Zhao","year":"2020","unstructured":"Zhao P, Luo A, Liu Y, Xu J, Li Z, Zhuang F, Sheng VS, Zhou X (2020) Where to go next: a spatio-temporal gated network for next POI recommendation. IEEE Trans Knowl Data Eng 34(5):2512\u20132524","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2093_CR28","doi-asserted-by":"crossref","unstructured":"He J, Qi J, Ramamohanarao K (2020) TimeSAN: a time-modulated self-attentive network for next point-of-interest recommendation. In: 2020 international joint conference on neural networks, pp 1\u20138","DOI":"10.1109\/IJCNN48605.2020.9207273"},{"key":"2093_CR29","doi-asserted-by":"crossref","unstructured":"Wu Y, Li K, Zhao G, Qian X (2019) Long-and short-term preference learning for next POI recommendation. In: the 28th ACM International Conference on Information and Knowledge Management, pp. 2301\u20132304","DOI":"10.1145\/3357384.3358171"},{"issue":"4","key":"2093_CR30","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1109\/TKDE.2020.3002531","volume":"34","author":"Y Wu","year":"2020","unstructured":"Wu Y, Li K, Zhao G, Qian X (2020) Personalized long-and short-term preference learning for next POI recommendation. IEEE Trans Knowl Data Eng 34(4):1944\u20131957","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2093_CR31","doi-asserted-by":"crossref","unstructured":"Thaipisutikul T, Chen Y-N (2023) An improved deep sequential model for context-aware POI recommendation. In: Multimedia tools and applications, pp 1\u201326","DOI":"10.1007\/s11042-023-15540-5"},{"issue":"1","key":"2093_CR32","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s10115-022-01749-7","volume":"65","author":"J Ren","year":"2023","unstructured":"Ren J, Gan M (2023) Mining dynamic preferences from geographical and interactive correlations for next POI recommendation. Knowl Inf Syst 65(1):183\u2013206","journal-title":"Knowl Inf Syst"},{"key":"2093_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109951","volume":"258","author":"W Chen","year":"2022","unstructured":"Chen W, Wan H, Guo S, Huang H, Zheng S, Li J, Lin S, Lin Y (2022) Building and exploiting spatial-temporal knowledge graph for next POI recommendation. Knowl-Based Syst 258:109951","journal-title":"Knowl-Based Syst"},{"key":"2093_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110052","volume":"258","author":"X Li","year":"2022","unstructured":"Li X, Hu R, Wang Z (2022) Next-point-of-interest recommendation based on joint mining of regularity and randomness. Knowl-Based Syst 258:110052","journal-title":"Knowl-Based Syst"},{"issue":"3731","key":"2093_CR35","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1126\/science.153.3731.34","volume":"153","author":"R Bellman","year":"1966","unstructured":"Bellman R (1966) Dynamic programming. Science 153(3731):34\u201337","journal-title":"Science"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02093-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-02093-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02093-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T07:29:18Z","timestamp":1719991758000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-02093-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,20]]},"references-count":35,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["2093"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-02093-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,20]]},"assertion":[{"value":"3 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}