{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T23:26:15Z","timestamp":1771543575783,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T00:00:00Z","timestamp":1690156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R &amp; D Program of China","award":["2022YFF0503900"],"award-info":[{"award-number":["2022YFF0503900"]}]},{"name":"National Key R &amp; D Program of China","award":["2021CXGC010104"],"award-info":[{"award-number":["2021CXGC010104"]}]},{"name":"Key R &amp; D Program of Shandong Province","award":["2022YFF0503900"],"award-info":[{"award-number":["2022YFF0503900"]}]},{"name":"Key R &amp; D Program of Shandong Province","award":["2021CXGC010104"],"award-info":[{"award-number":["2021CXGC010104"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>With the widespread use of the location-based social networks (LBSNs), the next point-of-interest (POI) recommendation has become an essential service, which aims to understand the user\u2019s check-in behavior at the current moment by analyzing and mining the correlations between the user\u2019s check-in behaviors within his\/her historical trajectory and then recommending the POI that the user is most likely to visit at the next time step. However, the user\u2019s check-in trajectory presents extremely irregular sequential patterns, such as spatial\u2013temporal patterns, semantic patterns, etc. Intuitively, the user\u2019s visiting behavior is often accompanied by a certain purpose, which makes the check-in data in LBSNs often have rich semantic activity characteristics. However, existing research mainly focuses on exploring the spatial\u2013temporal sequential patterns and lacks the mining of semantic information within the trajectory, so it is difficult to capture the user\u2019s visiting intention. In this paper, we propose a self-attention- and multi-task-based method, called MSAN, to explore spatial\u2013temporal and semantic sequential patterns simultaneously. Specifically, the MSAN proposes to mine the user\u2019s visiting intention from his\/her semantic sequence and uses the user\u2019s visiting intention prediction task as the auxiliary task of the next POI recommendation task. The user\u2019s visiting intention prediction uses hierarchical POI category attributes to describe the user\u2019s visiting intention and designs a hierarchical semantic encoder (HSE) to encode the hierarchical intention features. Moreover, a self-attention-based hierarchical intention-aware module (HIAM) is proposed to mine temporal and hierarchical intention features. The next POI recommendation uses the self-attention-based spatial\u2013temporal-aware module (STAM) to mine the spatial\u2013temporal sequential patterns within the user\u2019s check-in trajectory and fuses this with the hierarchical intention patterns to generate the next POI list. Experiments based on two real datasets verified the effectiveness of the model.<\/jats:p>","DOI":"10.3390\/ijgi12070297","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T01:12:28Z","timestamp":1690161148000},"page":"297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Next Point-of-Interest Recommendation Based on Joint Mining of Spatial\u2013Temporal and Semantic Sequential Patterns"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8976-3281","authenticated-orcid":false,"given":"Jing","family":"Tian","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7002-9306","authenticated-orcid":false,"given":"Zilin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Zhiming","family":"Ding","sequence":"additional","affiliation":[{"name":"The Institute of Software, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,24]]},"reference":[{"key":"ref_1","unstructured":"Zhao, S., King, I., and Lyu, M.R. (2016). A survey of point-of-interest recommendation in location-based social networks. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, Q., Wu, S., Wang, L., and Tan, T. (2016, January 12\u201317). Predicting the next location: A recurrent model with spatial and temporal contexts. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lian, D., Wu, Y., Ge, Y., Xie, X., and Chen, E. (2020, January 6\u201310). Geography-aware sequential location recommendation. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event.","DOI":"10.1145\/3394486.3403252"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Luo, Y., Liu, Q., and Liu, Z. (2021, January 19\u201323). Stan: Spatio-temporal attention network for next location recommendation. Proceedings of the Web Conference 2021, Ljubljana, Slovenia.","DOI":"10.1145\/3442381.3449998"},{"key":"ref_5","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_6","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_7","unstructured":"Guo, Q., and Qi, J. (2020). Sanst: A self-attentive network for next point-of-interest recommendation. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1109\/TKDE.2019.2903463","article-title":"A joint two-phase time-sensitive regularized collaborative ranking model for point of interest recommendation","volume":"32","author":"Aliannejadi","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cho, E., Myers, S.A., and Leskovec, J. (2011, January 21\u201324). Friendship and mobility: User movement in location-based social networks. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA.","DOI":"10.1145\/2020408.2020579"},{"key":"ref_10","unstructured":"Yuan, Q., Cong, G., Ma, Z., Sun, A., and Thalmann, N.M. (August, January 28). Time-aware point-of-interest recommendation. Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3057283","article-title":"A time-aware personalized point-of-interest recommendation via high-order tensor factorization","volume":"35","author":"Li","year":"2017","journal-title":"Acm Trans. Inf. Syst. (TOIS)"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gao, H., Tang, J., Hu, X., and Liu, H. (2013, January 12\u201316). Exploring temporal effects for location recommendation on location-based social networks. Proceedings of the 7th ACM Conference on Recommender Systems, Hong Kong, China.","DOI":"10.1145\/2507157.2507182"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yuan, N.J., Lian, D., Xu, L., Xie, X., Chen, E., and Rui, Y. (2015, January 10\u201313). Regularity and conformity: Location prediction using heterogeneous mobility data. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, Australia.","DOI":"10.1145\/2783258.2783350"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ye, M., Yin, P., Lee, W.C., and Lee, D.L. (2011, January 24\u201328). Exploiting geographical influence for collaborative point-of-interest recommendation. Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, China.","DOI":"10.1145\/2009916.2009962"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cheng, C., Yang, H., King, I., and Lyu, M. (2012, January 22\u201326). Fused matrix factorization with geographical and social influence in location-based social networks. Proceedings of the AAAI Conference on Artificial Intelligence, Toronto, ON, Canada.","DOI":"10.1609\/aaai.v26i1.8100"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chang, B., Park, Y., Park, D., Kim, S., and Kang, J. (2018, January 13\u201319). Content-aware hierarchical point-of-interest embedding model for successive poi recommendation. Proceedings of the IJCAI, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/458"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ference, G., Ye, M., and Lee, W.C. (2013, January 17\u201321). Location recommendation for out-of-town users in location-based social networks. Proceedings of the 22nd ACM International Conference on INFORMATION & Knowledge Management, Atlanta, GA, USA.","DOI":"10.1145\/2505515.2505637"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, H., Terrovitis, M., and Mamoulis, N. (2013, January 2\u20135). Location recommendation in location-based social networks using user check-in data. Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Beijing, China.","DOI":"10.1145\/2525314.2525357"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2523068","article-title":"Mining user check-in behavior with a random walk for urban point-of-interest recommendations","volume":"5","author":"Ying","year":"2014","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, D., Zhang, D., Yu, Z., and Wang, Z. (2013, January 1\u20133). A sentiment-enhanced personalized location recommendation system. Proceedings of the 24th ACM conference on Hypertext and Social Media, Paris, France.","DOI":"10.1145\/2481492.2481505"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2532439","article-title":"Personalized recommendations of locally interesting venues to tourists via cross-region community matching","volume":"5","author":"Zhao","year":"2014","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bao, J., Zheng, Y., and Mokbel, M.F. (2012, January 6\u20139). Location-based and preference-aware recommendation using sparse geo-social networking data. Proceedings of the 20th International Conference on Advances in Geographic Information Systems, Redondo Beach, CA, USA.","DOI":"10.1145\/2424321.2424348"},{"key":"ref_23","unstructured":"Liu, X., Liu, Y., Aberer, K., and Miao, C. (November, January 27). Personalized point-of-interest recommendation by mining users\u2019 preference transition. Proceedings of the Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, San Francisco, CA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, J.D., Chow, C.Y., and Li, Y. (2014, January 4\u20137). Lore: Exploiting sequential influence for location recommendations. Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Dallas, TX, USA.","DOI":"10.1145\/2666310.2666400"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kong, D., and Wu, F. (2018, January 13\u201319). HST-LSTM: A hierarchical spatial\u2013temporal long-short term memory network for location prediction. Proceedings of the IJCAI, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/324"},{"key":"ref_26","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_27","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","first-page":"3197","article-title":"Intention-Oriented Itinerary Recommendation Through Bridging Physical Trajectories and Online Social Networks","volume":"6","author":"Meng","year":"2012","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhao, P., Zhao, L., Liu, Y., Sheng, V.S., and Zhou, X. (2021, January 19\u201322). Variational self-attention network for sequential recommendation. Proceedings of the 2021 IEEE 37th International Conference on Data Engineering (ICDE), Chania, Greece.","DOI":"10.1109\/ICDE51399.2021.00138"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_31","unstructured":"Ba, J.L., Kiros, J.R., and Hinton, G.E. (2016). Layer normalization. arXiv."},{"key":"ref_32","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TSMC.2014.2327053","article-title":"Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs","volume":"45","author":"Yang","year":"2014","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, R., Lee, W.S., Ng, H.T., and Dahlmeier, D. (2019). An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. arXiv.","DOI":"10.18653\/v1\/P19-1048"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3464300","article-title":"Cha: Categorical hierarchy-based attention for next poi recommendation","volume":"40","author":"Zang","year":"2021","journal-title":"Acm Trans. Inf. Syst. (TOIS)"},{"key":"ref_36","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. 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