{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T23:39:15Z","timestamp":1780702755489,"version":"3.54.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61977013"],"award-info":[{"award-number":["61977013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s10489-022-04377-4","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T21:20:45Z","timestamp":1671139245000},"page":"16762-16775","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Global spatio-temporal aware graph neural network for next point-of-interest recommendation"],"prefix":"10.1007","volume":"53","author":[{"given":"Jingkuan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0805-7928","authenticated-orcid":false,"given":"Bo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haodong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongsheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"issue":"10","key":"4377_CR1","doi-asserted-by":"publisher","first-page":"3694","DOI":"10.1007\/s10489-019-01477-6","volume":"49","author":"H Shi","year":"2019","unstructured":"Shi H, Chen L, Xu Z, Lyu D (2019) Personalized location recommendation using mobile phone usage information. Appl Intell 49(10):3694\u20133707","journal-title":"Appl Intell"},{"key":"4377_CR2","unstructured":"Han P, Shang S, Sun A, Zhao P, Zheng K, Zhang X (2021) Point-of-interest recommendation with global and local context. IEEE Trans Knowl Data Eng 1\u20131"},{"key":"4377_CR3","doi-asserted-by":"crossref","unstructured":"Zhao P, Zhu H, Liu Y, Xu J, Li Z, Zhuang F, Sheng VS, Zhou X (2019) Where to go next: a spatio-temporal gated network for next poi recommendation. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), pp 5877\u20135884","DOI":"10.1609\/aaai.v33i01.33015877"},{"key":"4377_CR4","doi-asserted-by":"crossref","unstructured":"Zhao K, Zhang Y, Yin H, Wang J, Zheng K, Zhou X, Xing C (2020) Discovering subsequence patterns for next poi recommendation. In: Proceedings of the 29th international joint conference on artificial intelligence(IJCAI), pp 3216\u20133222","DOI":"10.24963\/ijcai.2020\/445"},{"issue":"4","key":"4377_CR5","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1007\/s10489-020-01868-0","volume":"51","author":"L Chang","year":"2021","unstructured":"Chang L, Chen W, Huang J, Bin C, Wang W (2021) Exploiting multi-attention network with contextual influence for point-of-interest recommendation. Appl Intell 51(4):1904\u20131917","journal-title":"Appl Intell"},{"issue":"3","key":"4377_CR6","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1007\/s10489-018-1276-1","volume":"49","author":"S Xing","year":"2019","unstructured":"Xing S, Liu F, Wang Q, Zhao X, Li T (2019) Content-aware point-of-interest recommendation based on convolutional neural network. Appl Intell 49(3):858\u2013871","journal-title":"Appl Intell"},{"key":"4377_CR7","doi-asserted-by":"crossref","unstructured":"Zhang F, Yuan NJ, Zheng K, Lian D, Xie X, Rui Y (2016) Exploiting dining preference for restaurant recommendation. In: Proceedings of the 25th international conference on World Wide Web (WWW), pp 725\u2013735","DOI":"10.1145\/2872427.2882995"},{"key":"4377_CR8","doi-asserted-by":"crossref","unstructured":"Lim KH, Chan J, Karunasekera S, Leckie C (2017) Personalized itinerary recommendation with queuing time awareness. In: Proceedings of the 40th International ACM SIGIR conference on research and development in information retrieval (SIGIR), pp 325\u2013334","DOI":"10.1145\/3077136.3080778"},{"key":"4377_CR9","doi-asserted-by":"publisher","first-page":"113070","DOI":"10.1016\/j.eswa.2019.113070","volume":"144","author":"L Chen","year":"2020","unstructured":"Chen L, Zhang L, Cao S, Wu Z, Cao J (2020) Personalized itinerary recommendation: Deep and collaborative learning with textual information. Expert Syst Appl 144:113070","journal-title":"Expert Syst Appl"},{"key":"4377_CR10","unstructured":"Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: successive point-of-interest recommendation. In: Proceedings of the 23th international joint conference on articial intelligence (IJCAI), pp 2605\u20132611"},{"key":"4377_CR11","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: Proceedings of the AAAI conference on artificial intelligence (AAAI), pp 194\u2013200","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"4377_CR12","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: Proceedings of the 2018 World Wide Web Conference (WWW), pp 1459\u20131468","DOI":"10.1145\/3178876.3186058"},{"key":"4377_CR13","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: Proceedings of the twenty-sixth international joint conference on artificial intelligence (IJCAI), vol 17, pp 3602\u20133608","DOI":"10.24963\/ijcai.2017\/504"},{"key":"4377_CR14","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: Proceedings of the AAAI conference on artificial intelligence (AAAI), vol 34, pp 214\u2013221","DOI":"10.1609\/aaai.v34i01.5353"},{"key":"4377_CR15","first-page":"1025","volume":"30","author":"W Hamilton","year":"2017","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Advances in Neural Information Processing Systems (NIPS) 30:1025\u20131035","journal-title":"Advances in Neural Information Processing Systems (NIPS)"},{"key":"4377_CR16","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A., Li\u00f2 P, Bengio Y (2017) Graph attention networks. 6th International conference on learning representations (ICLR) 1\u201312"},{"key":"4377_CR17","doi-asserted-by":"crossref","unstructured":"Chang B, Jang G, Kim S, Kang J (2020) Learning graph-based geographical latent representation for point-of-interest recommendation. In: Proceedings of the 29th ACM international conference on information and knowledge management (CIKM), pp 135\u2013144","DOI":"10.1145\/3340531.3411905"},{"key":"4377_CR18","doi-asserted-by":"crossref","unstructured":"Xie M, Yin H, Wang H, Xu F, Chen W, Wang S (2016) Learning graph-based poi embedding for location-based recommendation. In: Proceedings of the 25th ACM international conference on information and knowledge management (CIKM), pp 15\u201324","DOI":"10.1145\/2983323.2983711"},{"key":"4377_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2021.07.063","volume":"462","author":"J Zhang","year":"2021","unstructured":"Zhang J, Liu X, Zhou X, Chu X (2021) Leveraging graph neural networks for point-of-interest recommendations. Neurocomputing 462:1\u201313","journal-title":"Neurocomputing"},{"key":"4377_CR20","doi-asserted-by":"crossref","unstructured":"Kang W. -C., McAuley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE International conference on data mining (ICDM), pp 197\u2013206","DOI":"10.1109\/ICDM.2018.00035"},{"key":"4377_CR21","doi-asserted-by":"crossref","unstructured":"Lian D, Wu Y, Ge Y, Xie X, Chen E (2020) Geography-aware sequential location recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 2009\u20132019","DOI":"10.1145\/3394486.3403252"},{"key":"4377_CR22","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.neucom.2019.12.122","volume":"400","author":"T Liu","year":"2020","unstructured":"Liu T, Liao J, Wu Z, Wang Y, Wang J (2020) Exploiting geographical-temporal awareness attention for next point-of-interest recommendation. Neurocomputing 400:227\u2013237","journal-title":"Neurocomputing"},{"key":"4377_CR23","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0142, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems (NIPS), pp 5998\u20136008"},{"key":"4377_CR24","doi-asserted-by":"crossref","unstructured":"Wang H, Shen H, Ouyang W, Cheng X (2018) Exploiting poi-specific geographical influence for point-of-interest recommendation. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence (IJCAI), pp 3877\u20133883","DOI":"10.24963\/ijcai.2018\/539"},{"key":"4377_CR25","doi-asserted-by":"crossref","unstructured":"Yuan Z, Liu H, Liu Y, Zhang D, Yi F, Zhu N, Xiong H (2020) Spatio-temporal dual graph attention network for query-poi matching. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in information retrieval (SIGIR), pp 629\u2013638","DOI":"10.1145\/3397271.3401159"},{"issue":"5","key":"4377_CR26","doi-asserted-by":"publisher","first-page":"5310","DOI":"10.1007\/s10489-021-02677-9","volume":"52","author":"Y Liu","year":"2022","unstructured":"Liu Y, Yang Z, Li T, Wu D (2022) A novel poi recommendation model based on joint spatiotemporal effects and four-way interaction. Appl Intell 52(5):5310\u20135324","journal-title":"Appl Intell"},{"key":"4377_CR27","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations (ICLR) 1\u201314"},{"key":"4377_CR28","doi-asserted-by":"crossref","unstructured":"Yu F, Cui L, Guo W, Lu X, Li Q, Lu H (2020) A category-aware deep model for successive poi recommendation on sparse check-in data. In: Proceedings of the 2020 World Wide Web Conference (WWW), pp 1264\u20131274","DOI":"10.1145\/3366423.3380202"},{"key":"4377_CR29","doi-asserted-by":"crossref","unstructured":"Liu Y, Liu C, Lu X, Teng M, Zhu H, Xiong H (2017) Point-of-interest demand modeling with human mobility patterns. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 947\u2013955","DOI":"10.1145\/3097983.3098168"},{"key":"4377_CR30","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 2021 World Wide Web Conference (WWW), pp 2177\u20132185","DOI":"10.1145\/3442381.3449998"},{"key":"4377_CR31","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on World Wide Web (WWW), pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"issue":"1","key":"4377_CR32","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1007\/s11042-020-09746-0","volume":"80","author":"D Yu","year":"2021","unstructured":"Yu D, Wanyan W, Wang D (2021) Leveraging contextual influence and user preferences for point-of-interest recommendation. Multimed Tools Appl 80(1):1487\u20131501","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"4377_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3464300","volume":"40","author":"H Zang","year":"2022","unstructured":"Zang H, Han D, Li X, Wan Z, Wang M (2022) Cha: categorical hierarchy-based attention for next poi recommendation. ACM Transactions on Information Systems (TOIS) 40(1):1\u201322","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"4377_CR34","doi-asserted-by":"crossref","unstructured":"Zhou F, Yin R, Zhang K, Trajcevski G, Zhong T, Wu J (2019) Adversarial point-of-interest recommendation. In: The world wide web conference (WWW), pp 3462\u201334618","DOI":"10.1145\/3308558.3313609"},{"key":"4377_CR35","unstructured":"Feng S, Li X, Zeng Y, Cong G, Chee YM, Yuan Q (2015) Personalized ranking metric embedding for next new poi recommendation. In: Twenty-fourth international joint conference on artificial intelligence (IJCAI)"},{"key":"4377_CR36","doi-asserted-by":"crossref","unstructured":"Yao L, Sheng QZ, Qin Y, Wang X, Shemshadi A, He Q (2015) Context-aware point-of-interest recommendation using tensor factorization with social regularization. In: Proceedings of the 38th International ACM SIGIR conference on research and development in information retrieval (SIGIR), pp 1007\u20131010","DOI":"10.1145\/2766462.2767794"},{"key":"4377_CR37","doi-asserted-by":"crossref","unstructured":"Ye M, Yin P, Lee W-C (2010) Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp 458\u2013461","DOI":"10.1145\/1869790.1869861"},{"issue":"4","key":"4377_CR38","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.1007\/s10489-020-01921-y","volume":"51","author":"P Wen","year":"2021","unstructured":"Wen P, Yuan W, Qin Q, Sang S, Zhang Z (2021) Neural attention model for recommendation based on factorization machines. Appl Intell 51(4):1829\u20131844","journal-title":"Appl Intell"},{"issue":"2","key":"4377_CR39","doi-asserted-by":"publisher","first-page":"1913","DOI":"10.1007\/s10489-021-02497-x","volume":"52","author":"M Ma","year":"2022","unstructured":"Ma M, Na S, Wang H, Chen C, Xu J (2022) The graph-based behavior-aware recommendation for interactive news. Appl Intell 52(2):1913\u20131929","journal-title":"Appl Intell"},{"key":"4377_CR40","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.neucom.2021.05.114","volume":"472","author":"MA Islam","year":"2022","unstructured":"Islam MA, Mohammad MM, Das SSS, Ali ME (2022) A survey on deep learning based point-of-interest (poi) recommendations. Neurocomputing 472:306\u2013325","journal-title":"Neurocomputing"},{"key":"4377_CR41","doi-asserted-by":"crossref","unstructured":"Yang D, Fankhauser B, Rosso P, Cudre-Mauroux P (2020) Location prediction over sparse user mobility traces using rnns: Flashback in hidden states!. In: Proceedings of the twenty-ninth international joint conference on artificial intelligence (IJCAI), pp 2184\u20132190","DOI":"10.24963\/ijcai.2020\/302"},{"key":"4377_CR42","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 (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN48605.2020.9207273"},{"key":"4377_CR43","doi-asserted-by":"crossref","unstructured":"Zhang Y, Fu Y, Wang P, Li X, Zheng Y (2019) Unifying inter-region autocorrelation and intra-region structures for spatial embedding via collective adversarial learning. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 1700\u20131708","DOI":"10.1145\/3292500.3330972"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04377-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04377-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04377-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T05:10:01Z","timestamp":1688188201000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04377-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"references-count":43,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["4377"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04377-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,15]]},"assertion":[{"value":"29 November 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}