{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T20:03:49Z","timestamp":1768593829608,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["No. 61402150"],"award-info":[{"award-number":["No. 61402150"]}]},{"name":"National Natural Science Foundation of China","award":["61806074"],"award-info":[{"award-number":["61806074"]}]},{"name":"National Natural Science Foundation of China","award":["No.232102211029"],"award-info":[{"award-number":["No.232102211029"]}]},{"name":"National Natural Science Foundation of China","award":["No. 23A520016"],"award-info":[{"award-number":["No. 23A520016"]}]},{"name":"Science and Technology Research Project of Henan Province","award":["No. 61402150"],"award-info":[{"award-number":["No. 61402150"]}]},{"name":"Science and Technology Research Project of Henan Province","award":["61806074"],"award-info":[{"award-number":["61806074"]}]},{"name":"Science and Technology Research Project of Henan Province","award":["No.232102211029"],"award-info":[{"award-number":["No.232102211029"]}]},{"name":"Science and Technology Research Project of Henan Province","award":["No. 23A520016"],"award-info":[{"award-number":["No. 23A520016"]}]},{"name":"Key Scientific Research Project Plan of Colleges and Universities in Henan Province","award":["No. 61402150"],"award-info":[{"award-number":["No. 61402150"]}]},{"name":"Key Scientific Research Project Plan of Colleges and Universities in Henan Province","award":["61806074"],"award-info":[{"award-number":["61806074"]}]},{"name":"Key Scientific Research Project Plan of Colleges and Universities in Henan Province","award":["No.232102211029"],"award-info":[{"award-number":["No.232102211029"]}]},{"name":"Key Scientific Research Project Plan of Colleges and Universities in Henan Province","award":["No. 23A520016"],"award-info":[{"award-number":["No. 23A520016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Next Point-of-Interest (POI) recommendation has shown great value for both users and providers in location-based services. Existing methods mainly rely on partial information in users\u2019 check-in sequences, and are brittle to users with few interactions. Moreover, they ignore the impact of multi-dimensional auxiliary information such as user check-in frequency, POI category on user preferences modeling and the impact of dynamic changes in user preferences over different time periods on recommendation performance. To address the above limitations, we propose a novel method for next POI recommendation by modeling long and short term user preferences with multi-dimensional auxiliary information. In particular, the proposed model includes a static LSTM module to capture users\u2019 multi-dimensional long term static preferences and a dynamic meta-learning module to capture users\u2019 multi-dimensional dynamic preferences. Furthermore, we incorporate a POI category filter into our model to comprehensively simulate users\u2019 preferences. Experimental results on two real-world datasets demonstrate that our model outperforms the state-of-the-art baseline methods in two commonly used evaluation metrics.<\/jats:p>","DOI":"10.3390\/ijgi12090352","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T08:33:09Z","timestamp":1692952389000},"page":"352","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Modeling Long and Short Term User Preferences by Leveraging Multi-Dimensional Auxiliary Information for Next POI Recommendation"],"prefix":"10.3390","volume":"12","author":[{"given":"Zheng","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Laboratory of Spatial Information Processing, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyuan","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liupeng","family":"Gong","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9388-5690","authenticated-orcid":false,"given":"Ke","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1109\/TSC.2019.2918310","article-title":"An attention-based spatiotemporal lstm network for next poi recommendation","volume":"14","author":"Huang","year":"2019","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_2","first-page":"100161","article-title":"How can we create a recommender system for tourism? A location centric spatial binning-based methodology using social networks","volume":"3","author":"Acharya","year":"2023","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1007\/s42979-023-01683-7","article-title":"Differential privacy-based social network detection over spatio-temporal proximity for secure POI recommendation","volume":"4","author":"Acharya","year":"2023","journal-title":"SN Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, Y., Zhou, X., Leng, Z., and Wang, X. (2022). Long-and short-term preference modeling based on multi-level attention for next POI recommendation. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11060323"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zou, Z., He, X., and Zhu, A.X. (2019). An automatic annotation method for discovering semantic information of geographical locations from location-based social networks. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8110487"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, J., Ding, K., and Caverlee, J. (2021, January 11\u201315). Sequential recommendation for cold-start users with meta transitional learning. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event.","DOI":"10.1145\/3404835.3463089"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, S., Guo, J., Liu, C., Li, Z., and Li, R. (2023, January 29\u201331). Next point-of-interest recommendation for cold-start users with spatial-temporal meta-learning. Proceedings of the 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), Shenyang, China.","DOI":"10.1109\/ICPECA56706.2023.10075890"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3545118","article-title":"Doing more with less: Overcoming data scarcity for poi recommendation via cross-region transfer","volume":"13","author":"Gupta","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yao, Z., Fu, Y., Liu, B., Liu, Y., and Xiong, H. (2016, January 12\u201315). POI recommendation: A temporal matching between POI popularity and user regularity. Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain.","DOI":"10.1109\/ICDM.2016.0066"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Si, Y., Zhang, F., and Liu, W. (2017, January 22\u201323). A time-aware poi recommendation method exploiting user-based collaborative filtering and location popularity. Proceedings of the 2017 2nd International Conference on Communications, Information Management and Network Security, Beijing, China.","DOI":"10.12783\/dtcse\/cimns2017\/16270"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wu, Y., Li, K., Zhao, G., and Qian, X. (2019, January 3\u20137). Long-and short-term preference learning for next POI recommendation. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China.","DOI":"10.1145\/3357384.3358171"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sun, K., Qian, T., Chen, T., Liang, Y., Nguyen, Q.V.H., and Yin, H. (2020, January 7\u201312). Where to go next: Modeling long-and short-term user preferences for point-of-interest recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i01.5353"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/TCSS.2020.3036661","article-title":"Memory augmented hierarchical attention network for next point-of-interest recommendation","volume":"8","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3174","DOI":"10.1002\/int.22412","article-title":"An attention-based category-aware GRU model for the next POI recommendation","volume":"36","author":"Liu","year":"2021","journal-title":"Int. J. Intell. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, R., Shen, Y., and Zhu, Y. (2018, January 17\u201320). Next point-of-interest recommendation with temporal and multi-level context attention. Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), Singapore.","DOI":"10.1109\/ICDM.2018.00144"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2512","DOI":"10.1109\/TKDE.2020.3007194","article-title":"Where to go next: A spatio-temporal gated network for next poi recommendation","volume":"34","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_17","unstructured":"Cui, Q., Zhang, Y., and Wang, J. (2021). CANS-Net: Context-aware non-successive modeling network for next Point-of-Interest recommendation. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, C., Liu, J., Wang, J., Xu, S., Han, H., and Chen, Y. (2019). An attention-based spatiotemporal gated recurrent unit network for point-of-interest recommendation. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8080355"},{"key":"ref_19","first-page":"1","article-title":"Learning from hometown and current city: Cross-city POI recommendation via interest drift and transfer learning","volume":"3","author":"Ding","year":"2019","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2566","DOI":"10.1109\/TKDE.2016.2580511","article-title":"Adapting to user interest drift for poi recommendation","volume":"28","author":"Yin","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1287\/ijoc.2023.1286","article-title":"TGVx: Dynamic personalized POI deep recommendation model","volume":"35","author":"Wang","year":"2023","journal-title":"INFORMS J. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Feng, J., Li, Y., Zhang, C., Sun, F., Meng, F., Guo, A., and Jin, D. (2018, January 23\u201327). Deepmove: Predicting human mobility with attentional recurrent networks. Proceedings of the 2018 World Wide Web Conference, Lyon, France.","DOI":"10.1145\/3178876.3186058"},{"key":"ref_23","unstructured":"Bengio, S., Bengio, Y., Cloutier, J., and Gecsei, J. (,  2002). On the optimization of a synaptic learning rule. Proceedings of the Preprints Conference Optimality in Artificial and Biological Neural Networks, Montreal, QC, Canada."},{"key":"ref_24","unstructured":"Li, J., Jing, M., Lu, K., Zhu, L., Yang, Y., and Huang, Z. (February, January 27). From zero-shot learning to cold-start recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lee, H., Im, J., Jang, S., Cho, H., and Chung, S. (2019, January 4\u20138). Melu: Meta-learned user preference estimator for cold-start recommendation. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330859"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dong, M., Yuan, F., Yao, L., Xu, X., and Zhu, L. (2020, January 6\u201310). Mamo: Memory-augmented meta-optimization for cold-start recommendation. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event.","DOI":"10.1145\/3394486.3403113"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pan, F., Li, S., Ao, X., Tang, P., and He, Q. (2019, January 21\u201325). Warm up cold-start advertisements: Improving ctr predictions via learning to learn id embeddings. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France.","DOI":"10.1145\/3331184.3331268"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bharadhwaj, H. (2019, January 14\u201319). Meta-learning for user cold-start recommendation. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852100"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lu, Y., Fang, Y., and Shi, C. (2020, January 6\u201310). Meta-learning on heterogeneous information networks for cold-start recommendation. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event.","DOI":"10.1145\/3394486.3403207"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, X., Fan, M., Huang, J., Yang, S., and Zhu, W. (2021, January 14\u201318). Curriculum meta-learning for next POI recommendation. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore.","DOI":"10.1145\/3447548.3467132"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tan, H., Yao, D., Huang, T., Wang, B., Jing, Q., and Bi, J. (2021, January 15\u201318). Meta-learning enhanced neural ODE for citywide next POI recommendation. Proceedings of the 2021 22nd IEEE International Conference on Mobile Data Management (MDM), Toronto, ON, Canada.","DOI":"10.1109\/MDM52706.2021.00023"},{"key":"ref_32","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017, January 10\u201315). Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, I.C., Lu, Y.S., Shih, W.Y., and Huang, J.L. (2018, January 23\u201327). Successive poi recommendation with category transition and temporal influence. Proceedings of the 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan.","DOI":"10.1109\/COMPSAC.2018.10203"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Su, Y., Li, X., Liu, B., Zha, D., Xiang, J., Tang, W., and Gao, N. (2020, January 7\u201311). Fgcrec: Fine-grained geographical characteristics modeling for point-of-interest recommendation. Proceedings of the ICC 2020-2020 IEEE International Conference on Communications (ICC), Virtual Event.","DOI":"10.1109\/ICC40277.2020.9148797"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rendle, S., Gantner, Z., Freudenthaler, C., and Schmidt-Thieme, L. (2011, January 24\u201328). Fast context-aware recommendations with factorization machines. Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, China.","DOI":"10.1145\/2009916.2010002"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, X., and Chua, T.S. (2017, January 7\u201311). Neural factorization machines for sparse predictive analytics. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan.","DOI":"10.1145\/3077136.3080777"},{"key":"ref_37","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2015). Session-based recommendations with recurrent neural networks. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tang, J., and Wang, K. (2018, January 21\u201325). Personalized top-n sequential recommendation via convolutional sequence embedding. Proceedings of the 11th ACM International Conference on Web Search and Data Mining, Virtual Event.","DOI":"10.1145\/3159652.3159656"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kang, W.C., and McAuley, J. (2018, January 17\u201320). Self-attentive sequential recommendation. Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), Singapore.","DOI":"10.1109\/ICDM.2018.00035"},{"key":"ref_40","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.S. (2017, January 3\u20137). Neural collaborative filtering. Proceedings of the 26th International Conference on World Wide Web, Perth, Australia.","DOI":"10.1145\/3038912.3052569"},{"key":"ref_42","unstructured":"Hassani, K., and Khasahmadi, A.H. (2021, January 18\u201324). Contrastive multi-view representation learning on graphs. Proceedings of the International Conference on Machine Learning, Virtual Event."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/9\/352\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:39:02Z","timestamp":1760128742000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/9\/352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,25]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["ijgi12090352"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12090352","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,25]]}}}