{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T05:50:19Z","timestamp":1773208219984,"version":"3.50.1"},"reference-count":168,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"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":["Geoinformatica"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s10707-024-00531-x","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T05:16:25Z","timestamp":1730092585000},"page":"305-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Exploring the evolution, progress, and future of point-of-interest recommendation over location-based social network: a comprehensive review"],"prefix":"10.1007","volume":"29","author":[{"given":"Malika","family":"Acharya","sequence":"first","affiliation":[]},{"given":"Krishna Kumar","family":"Mohbey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"531_CR1","unstructured":"Cairncross F (2001) The death of distance: How the communications revolution is changing our lives\u2013distance isn\u2019t what it used to be. Harvard Business Review. Retrieved March, 13, 2018"},{"key":"531_CR2","doi-asserted-by":"crossref","unstructured":"Gao H, Liu H (2013) Data analysis on location-based social networks. Mobile social networking: an innovative approach. Springer New York, New York, pp 165\u2013194","DOI":"10.1007\/978-1-4614-8579-7_8"},{"issue":"4","key":"531_CR3","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1177\/0894439313483689","volume":"31","author":"E Bahir","year":"2013","unstructured":"Bahir E, Peled A (2013) Identifying and tracking major events using geo-social networks. Social Sci Comput Rev 31(4):458\u2013470","journal-title":"Social Sci Comput Rev"},{"issue":"2","key":"531_CR4","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1109\/TCC.2016.2517638","volume":"8","author":"Z Xu","year":"2016","unstructured":"Xu Z, Liu Y, Yen NY, Mei L, Luo X, Wei X, Hu C (2016) Crowdsourcing based description of urban emergency events using social media big data. IEEE Trans Cloud Comput 8(2):387\u2013397","journal-title":"IEEE Trans Cloud Comput"},{"key":"531_CR5","doi-asserted-by":"publisher","first-page":"11567","DOI":"10.1007\/s11042-015-2731-1","volume":"76","author":"Z Xu","year":"2017","unstructured":"Xu Z, Liu Y, Xuan J, Chen H, Mei L (2017) Crowdsourcing based social media data analysis of urban emergency events. Multimedia Tools Appl 76:11567\u201311584","journal-title":"Multimedia Tools Appl"},{"issue":"06","key":"531_CR6","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/MIS.2012.6","volume":"27","author":"J Yin","year":"2012","unstructured":"Yin J, Lampert A, Cameron M, Robinson B, Power R (2012) Using social media to enhance emergency situation awareness. IEEE Intell Syst 27(06):52\u201359","journal-title":"IEEE Intell Syst"},{"key":"531_CR7","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.cities.2016.03.013","volume":"55","author":"Y Shen","year":"2016","unstructured":"Shen Y, Karimi K (2016) Urban function connectivity: Characterisation of functional urban streets with social media check-in data. Cities 55:9\u201321","journal-title":"Cities"},{"key":"531_CR8","doi-asserted-by":"crossref","unstructured":"Zhou X, Hristova D, Noulas A, Mascolo C (2017) Detecting socio-economic impact of cultural investment through geo-social network analysis. In: Proceedings of the International AAAI Conference on Web and Social Media (Vol. 11, No. 1, pp. 720\u2013723)","DOI":"10.1609\/icwsm.v11i1.14936"},{"key":"531_CR9","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10707-019-00373-y","volume":"24","author":"L Chen","year":"2020","unstructured":"Chen L, Shang S, Yang C, Li J (2020) Spatial keyword search: a survey. GeoInformatica 24:85\u2013106","journal-title":"GeoInformatica"},{"key":"531_CR10","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/s10707-014-0220-8","volume":"19","author":"J Bao","year":"2015","unstructured":"Bao J, Zheng Y, Wilkie D, Mokbel M (2015) Recommendations in location-based social networks: a survey. GeoInformatica 19:525\u2013565","journal-title":"GeoInformatica"},{"key":"531_CR11","unstructured":"Zhao S, King I, Lyu MR (2016) A survey of point-of-interest recommendation in location-based social networks. arXiv Preprint arXiv :160700647"},{"key":"531_CR12","doi-asserted-by":"crossref","unstructured":"Werneck H, Silva N, Viana MC, Mour\u00e3o F, Pereira AC, Rocha L (2020) A survey on point-of-interest recommendation in location-based social networks. In: Proceedings of the Brazilian Symposium on Multimedia and the Web, pp 185\u2013192","DOI":"10.1145\/3428658.3430970"},{"issue":"11s","key":"531_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3510409","volume":"54","author":"P S\u00e1nchez","year":"2022","unstructured":"S\u00e1nchez P, Bellog\u00edn A (2022) Point-of-interest recommender systems based on location-based social networks: a survey from an experimental perspective. ACM Comput Surv (CSUR) 54(11s):1\u201337","journal-title":"ACM Comput Surv (CSUR)"},{"key":"531_CR14","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s10707-020-00412-z","volume":"25","author":"S Singh","year":"2021","unstructured":"Singh S, Singh J, Singh S (2021) Mitigating spoofed GNSS trajectories through nature inspired algorithm. Geoinformatica 25:581\u2013600","journal-title":"Geoinformatica"},{"key":"531_CR15","doi-asserted-by":"publisher","first-page":"35997","DOI":"10.1109\/ACCESS.2021.3061502","volume":"9","author":"M Li","year":"2021","unstructured":"Li M, Zheng W, Xiao Y, Zhu K, Huang W (2021) Exploring temporal and spatial features for next POI recommendation in LBSNs. IEEE Access 9:35997\u201336007","journal-title":"IEEE Access"},{"key":"531_CR16","doi-asserted-by":"crossref","unstructured":"Su C, Jia XT, Xie XZ, Li N (2017) Community detection and location recommendation based on LBSN. In: 2017 International Conference on Network and Information Systems for Computers (ICNISC), IEEE, Shanghai, pp 227\u2013231","DOI":"10.1109\/ICNISC.2017.00056"},{"issue":"1","key":"531_CR17","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1108\/IJPCC-01-2016-0010","volume":"12","author":"J Han","year":"2016","unstructured":"Han J, Yamana H (2016) A study on individual mobility patterns based on individuals\u2019 familiarity to visited areas. Int J Pervasive Comput Commun 12(1):23\u201348","journal-title":"Int J Pervasive Comput Commun"},{"key":"531_CR18","doi-asserted-by":"crossref","unstructured":"Wang W, Yin H, Chen L, Sun Y, Sadiq S, Zhou X (2015) Geo-SAGE: A geographical sparse additive generative model for spatial item recommendation. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery And Data Mining, pp 1255\u20131264","DOI":"10.1145\/2783258.2783335"},{"key":"531_CR19","doi-asserted-by":"crossref","unstructured":"Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y (2014) GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 831\u2013840","DOI":"10.1145\/2623330.2623638"},{"key":"531_CR20","doi-asserted-by":"crossref","unstructured":"Liu B, Xiong H (2013) Point-of-interest recommendation in location based social networks with topic and location awareness. In: Proceedings of the 2013 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, pp 396\u2013404","DOI":"10.1137\/1.9781611972832.44"},{"key":"531_CR21","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1007\/s11036-018-1059-2","volume":"24","author":"R Logesh","year":"2019","unstructured":"Logesh R, Subramaniyaswamy V, Vijayakumar V, Li X (2019) Efficient user profiling based intelligent travel recommender system for individual and group of users. Mob Networks Appl 24:1018\u20131033","journal-title":"Mob Networks Appl"},{"key":"531_CR22","doi-asserted-by":"crossref","unstructured":"Yao Z, Fu Y, Liu B, Liu Y, Xiong H (2016) POI recommendation: A temporal matching between POI popularity and user regularity. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), IEEE, Barcelona, pp 549\u2013558","DOI":"10.1109\/ICDM.2016.0066"},{"key":"531_CR23","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: IJCAI, pp 3877\u20133883","DOI":"10.24963\/ijcai.2018\/539"},{"issue":"10","key":"531_CR24","doi-asserted-by":"publisher","first-page":"2566","DOI":"10.1109\/TKDE.2016.2580511","volume":"28","author":"H Yin","year":"2016","unstructured":"Yin H, Zhou X, Cui B, Wang H, Zheng K, Nguyen QVH (2016) Adapting to user interest drift for poi recommendation. IEEE Trans Knowl Data Eng 28(10):2566\u20132581","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"531_CR25","doi-asserted-by":"crossref","unstructured":"Ju W, Qin Y, Qiao Z, Luo X, Wang Y, Fu Y, Zhang M (2022) Kernel-based substructure exploration for next POI Recommendation. arXiv preprint arXiv:2210.03969","DOI":"10.1109\/ICDM54844.2022.00032"},{"key":"531_CR26","doi-asserted-by":"crossref","unstructured":"Su C, Wang J, Xie X (2020) Point-of-interest recommendation based on geographical influence and extended pairwise ranking. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, pp 966\u2013971","DOI":"10.1109\/INFOCOMWKSHPS50562.2020.9162997"},{"issue":"4","key":"531_CR27","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0266340","volume":"17","author":"K Li","year":"2022","unstructured":"Li K, Wei H, He X, Tian Z (2022) Relational POI recommendation model combined with geographic information. PLoS ONE 17(4):e0266340","journal-title":"PLoS ONE"},{"key":"531_CR28","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.knosys.2017.04.013","volume":"128","author":"Y Si","year":"2017","unstructured":"Si Y, Zhang F, Liu W (2017) CTF-ARA: an adaptive method for POI recommendation based on check-in and temporal features. Knowl Based Syst 128:59\u201370","journal-title":"Knowl Based Syst"},{"key":"531_CR29","doi-asserted-by":"crossref","unstructured":"Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 363\u2013372","DOI":"10.1145\/2484028.2484030"},{"key":"531_CR30","doi-asserted-by":"crossref","unstructured":"Zhang K, Jin Q, Pelechrinis K, Lappas T (2013) On the importance of temporal dynamics in modeling urban activity. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, pp 1\u20138","DOI":"10.1145\/2505821.2505825"},{"key":"531_CR31","doi-asserted-by":"crossref","unstructured":"Wang H, Terrovitis M, Mamoulis N (2013) Location recommendation in location-based social networks using user check-in data. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp 374\u2013383","DOI":"10.1145\/2525314.2525357"},{"key":"531_CR32","doi-asserted-by":"crossref","unstructured":"Zhang JD, Chow CY (2015) Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 443\u2013452","DOI":"10.1145\/2766462.2767711"},{"key":"531_CR33","doi-asserted-by":"crossref","unstructured":"Hu B, Ester M (2014) Social topic modeling for point-of-interest recommendation in location-based social networks. In: 2014 IEEE International Conference on Data Mining, IEEE, pp 845\u2013850","DOI":"10.1109\/ICDM.2014.124"},{"key":"531_CR34","doi-asserted-by":"crossref","unstructured":"Yang D, Qu B, Yang J, Cudre-Mauroux P (2019) Revisiting user mobility and social relationships in lbsns: a hypergraph embedding approach. In: The World Wide Web conference, pp 2147\u20132157","DOI":"10.1145\/3308558.3313635"},{"key":"531_CR35","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s12652-012-0117-z","volume":"5","author":"X Xiao","year":"2014","unstructured":"Xiao X, Zheng Y, Luo Q, Xie X (2014) Inferring social ties between users with human location history. J Ambient Intell Humaniz Comput 5:3\u201319","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"531_CR36","doi-asserted-by":"crossref","unstructured":"Ying JJC, Lu EHC, Lee WC, Weng TC, Tseng VS (2010) Mining user similarity from semantic trajectories. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, pp 19\u201326","DOI":"10.1145\/1867699.1867703"},{"key":"531_CR37","doi-asserted-by":"crossref","unstructured":"Wen YT, Lei PR, Peng WC, Zhou XF (2014) Exploring social influence on location-based social networks. In: 2014 IEEE International Conference on Data Mining, IEEE, pp 1043\u20131048","DOI":"10.1109\/ICDM.2014.66"},{"key":"531_CR38","doi-asserted-by":"crossref","unstructured":"Riboni D, Bettini C (2012) Private context-aware recommendation of points of interest: An initial investigation. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, IEEE, pp 584\u2013589","DOI":"10.1109\/PerComW.2012.6197582"},{"issue":"2","key":"531_CR39","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/jsan5020010","volume":"5","author":"PS Efraimidis","year":"2016","unstructured":"Efraimidis PS, Drosatos G, Arampatzis A, Stamatelatos G, Athanasiadis IN (2016) A privacy-by-design contextual suggestion system for tourism. J Sens Actuator Networks 5(2):10","journal-title":"J Sens Actuator Networks"},{"key":"531_CR40","doi-asserted-by":"publisher","first-page":"50031","DOI":"10.1109\/ACCESS.2019.2911107","volume":"7","author":"H Wu","year":"2019","unstructured":"Wu H, Li M, Zhang H (2019) Enabling smart anonymity scheme for security collaborative enhancement in location-based services. IEEE Access 7:50031\u201350040","journal-title":"IEEE Access"},{"key":"531_CR41","doi-asserted-by":"crossref","unstructured":"Zhuang Z, Wei T, Liu L, Qi H, Shen Y, Yin B (2024) TAU: Trajectory Data Augmentation with Uncertainty for Next POI Recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 38, no 20, pp 22565\u201322573","DOI":"10.1609\/aaai.v38i20.30265"},{"issue":"1","key":"531_CR42","first-page":"1","volume":"35","author":"C Zhang","year":"2016","unstructured":"Zhang C, Liang H, Wang K (2016) Trip recommendation meets real-world constraints: POI availability, diversity, and traveling time uncertainty. ACM Trans Inform Syst (TOIS) 35(1):1\u201328","journal-title":"ACM Trans Inform Syst (TOIS)"},{"issue":"7","key":"531_CR43","doi-asserted-by":"publisher","first-page":"4522","DOI":"10.1109\/TSMC.2023.3252079","volume":"53","author":"F Zhou","year":"2023","unstructured":"Zhou F, Qian T, Mo Y, Cheng Z, Xiao C, Wu J, Trajcevski G (2023) Uncertainty-aware heterogeneous representation learning in POI recommender systems. IEEE Trans Syst Man Cybernetics: Syst 53(7):4522\u20134535","journal-title":"IEEE Trans Syst Man Cybernetics: Syst"},{"key":"531_CR44","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s40558-017-0100-9","volume":"19","author":"C Trattner","year":"2018","unstructured":"Trattner C, Oberegger A, Marinho L, Parra D (2018) Investigating the utility of the weather context for point of interest recommendations. Inform Technol Tourism 19:117\u2013150","journal-title":"Inform Technol Tourism"},{"key":"531_CR45","doi-asserted-by":"publisher","first-page":"36299","DOI":"10.1109\/ACCESS.2022.3161941","volume":"10","author":"MB Hossain","year":"2022","unstructured":"Hossain MB, Arefin MS, Sarker IH, Kowsher M, Dhar PK, Koshiba T (2022) CARAN: a context-aware recency-based attention network for point-of-interest recommendation. IEEE Access 10:36299\u201336310","journal-title":"IEEE Access"},{"issue":"4","key":"531_CR46","doi-asserted-by":"publisher","first-page":"258","DOI":"10.3390\/ijgi10040258","volume":"10","author":"D Yu","year":"2021","unstructured":"Yu D, Shen Y, Xu K, Xu Y (2021) Context-specific point-of-interest recommendation based on popularity-weighted random sampling and factorization machine. ISPRS Int J Geo-Information 10(4):258","journal-title":"ISPRS Int J Geo-Information"},{"key":"531_CR47","doi-asserted-by":"crossref","unstructured":"Kang EY, Kim H, Cho J (2006) Personalization method for tourist point of interest (POI) recommendation. In: Knowledge-Based Intelligent Information and Engineering Systems: 10th International Conference, KES 2006, Bournemouth, UK, October 9\u201311, 2006. Proceedings, Part I 10 (pp. 392\u2013400). Springer, Berlin Heidelberg","DOI":"10.1007\/11892960_48"},{"key":"531_CR48","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.neucom.2015.10.146","volume":"210","author":"J Chen","year":"2016","unstructured":"Chen J, Li X, Cheung WK, Li K (2016) Effective successive POI recommendation inferred with individual behavior and group preference. Neurocomputing 210:174\u2013184","journal-title":"Neurocomputing"},{"key":"531_CR49","doi-asserted-by":"crossref","unstructured":"Horozov T, Narasimhan N, Vasudevan V (2006) Using location for personalized POI recommendations in mobile environments. In: International Symposium on Applications and the Internet (SAINT\u201906), IEEE, p 6-pp","DOI":"10.1109\/SAINT.2006.55"},{"issue":"6","key":"531_CR50","first-page":"907","volume":"17","author":"S Jiang","year":"2015","unstructured":"Jiang S, Qian X, Shen J, Fu Y, Mei T (2015) Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Trans Multimed 17(6):907\u2013918","journal-title":"IEEE Trans Multimed"},{"issue":"6","key":"531_CR51","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3390\/ijgi8060279","volume":"8","author":"M Gan","year":"2019","unstructured":"Gan M, Gao L (2019) Discovering memory-based preferences for POI recommendation in location-based social networks. ISPRS Int J Geo-Information 8(6):279","journal-title":"ISPRS Int J Geo-Information"},{"issue":"3","key":"531_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3182166","volume":"36","author":"D Lian","year":"2018","unstructured":"Lian D, Zheng K, Ge Y, Cao L, Chen E, Xie X (2018) GeoMF\u2009+\u2009+\u2009scalable location recommendation via joint geographical modeling and matrix factorization. ACM Trans Inform Syst (TOIS) 36(3):1\u201329","journal-title":"ACM Trans Inform Syst (TOIS)"},{"key":"531_CR53","doi-asserted-by":"crossref","unstructured":"Li X, Cong G, Li XL, Pham TAN, Krishnaswamy S (2015) Rank-geofm: A ranking based geographical factorization method for point of interest recommendation. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 433\u2013442","DOI":"10.1145\/2766462.2767722"},{"key":"531_CR54","doi-asserted-by":"crossref","unstructured":"Rahmani HA, Aliannejadi M, Ahmadian S, Baratchi M, Afsharchi M, Crestani F (2020) LGLMF: local geographical based logistic matrix factorization model for POI recommendation. In: Information Retrieval Technology: 15th Asia Information Retrieval Societies Conference, AIRS 2019, Hong Kong, China, November 7\u20139, 2019, Proceedings 15, Springer International Publishing, pp 66\u201378","DOI":"10.1007\/978-3-030-42835-8_7"},{"key":"531_CR55","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 80:1487\u20131501","journal-title":"Multimed Tools Appl"},{"key":"531_CR56","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s10115-020-01509-5","volume":"63","author":"M Davtalab","year":"2021","unstructured":"Davtalab M, Alesheikh AA (2021) A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization. Knowl Inf Syst 63:65\u201385","journal-title":"Knowl Inf Syst"},{"key":"531_CR57","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neucom.2017.02.005","volume":"241","author":"X Ren","year":"2017","unstructured":"Ren X, Song M, Haihong E, Song J (2017) Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation. Neurocomputing 241:38\u201355","journal-title":"Neurocomputing"},{"key":"531_CR58","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.neucom.2019.07.040","volume":"363","author":"L Hu","year":"2019","unstructured":"Hu L, Xing Y, Gong Y, Zhao K, Wang F (2019) Nonnegative matrix tri-factorization with user similarity for clustering in point-of-interest. Neurocomputing 363:58\u201365","journal-title":"Neurocomputing"},{"key":"531_CR59","doi-asserted-by":"crossref","unstructured":"Liu B, Fu Y, Yao Z, Xiong H (2013) Learning geographical preferences for point-of-interest recommendation. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1043\u20131051","DOI":"10.1145\/2487575.2487673"},{"key":"531_CR60","doi-asserted-by":"crossref","unstructured":"Liu Y, Wei W, Sun A, Miao C (2014) Exploiting geographical neighborhood characteristics for location recommendation. In: Proceedings of the 23rd ACM International conference on conference on Information and Knowledge Management, pp 739\u2013748","DOI":"10.1145\/2661829.2662002"},{"key":"531_CR61","doi-asserted-by":"crossref","unstructured":"Ying H, Chen L, Xiong Y, Wu J (2016) PGRank: Personalized geographical ranking for point-of-interest recommendation. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp 137\u2013138","DOI":"10.1145\/2872518.2889378"},{"issue":"4","key":"531_CR62","doi-asserted-by":"publisher","DOI":"10.3390\/info9040085","volume":"9","author":"L Guo","year":"2018","unstructured":"Guo L, Jiang H, Wang X (2018) Location regularization-based poi recommendation in location-based social networks. Information 9(4):85","journal-title":"Information"},{"key":"531_CR63","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1007\/s11063-017-9681-8","volume":"47","author":"S Zhao","year":"2018","unstructured":"Zhao S, King I, Lyu MR (2018) Aggregated temporal tensor factorization model for point-of-interest recommendation. Neural Process Lett 47:975\u2013992","journal-title":"Neural Process Lett"},{"key":"531_CR64","first-page":"1","volume":"2018","author":"J Liao","year":"2018","unstructured":"Liao J, Tang J, Zhao X, Shang H (2018) Improving poi recommendation via dynamic tensor completion. Sci Program 2018:1\u201311","journal-title":"Sci Program"},{"key":"531_CR65","unstructured":"Murakami T, Hamada K, Kawamoto Y, Hatano T (2019) Privacy-preserving multiple tensor factorization for synthesizing large-scale location traces with cluster-specific features. arXiv preprint arXiv:1911.04226"},{"key":"531_CR66","doi-asserted-by":"crossref","unstructured":"Tan H, Yao D, Huang T, Wang B, Jing Q, Bi J (2021) Meta-learning enhanced neural ODE for citywide next POI recommendation. In: 2021 22nd IEEE International Conference on Mobile Data Management (MDM), IEEE, pp 89\u201398","DOI":"10.1109\/MDM52706.2021.00023"},{"key":"531_CR67","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s12525-020-00416-5","volume":"31","author":"L Kang","year":"2021","unstructured":"Kang L, Liu S, Gong D, Tang M (2021) A personalized point-of-interest recommendation system for O2O commerce. Electron Markets 31:253\u2013267","journal-title":"Electron Markets"},{"key":"531_CR68","doi-asserted-by":"crossref","unstructured":"Sun H, Xu J, Zheng K, Zhao P, Chao P, Zhou X (2021) MFNP: A Meta-optimized Model for Few-shot Next POI Recommendation. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), pp 3017\u20133023","DOI":"10.24963\/ijcai.2021\/415"},{"key":"531_CR69","doi-asserted-by":"crossref","unstructured":"Zhou M, Li K, Dai K, Wu Q (2023) HIN-based rating prediction in recommender systems via GCN and meta-learning. Appl Intell 53:1\u201316","DOI":"10.1007\/s10489-023-04769-0"},{"issue":"3","key":"531_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3511711","volume":"13","author":"V Gupta","year":"2022","unstructured":"Gupta V, Bedathur S (2022) Doing more with less: overcoming data scarcity for poi recommendation via cross-region transfer. ACM Trans Intell Syst Technol (TIST) 13(3):1\u201324","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"531_CR71","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang X, Fan M, Huang J, Yang S, Zhu W (2021) Curriculum meta-learning for next POI recommendation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp 2692\u20132702","DOI":"10.1145\/3447548.3467132"},{"key":"531_CR72","doi-asserted-by":"crossref","unstructured":"Wang D, Liu K, Xiong H, Fu Y (2022) Online poi recommendation: learning dynamic geo-human interactions in streams. IEEE Trans Big Data 9(3):832-844","DOI":"10.1109\/TBDATA.2022.3215134"},{"key":"531_CR73","doi-asserted-by":"crossref","unstructured":"Massimo D, Ricci F (2018) Harnessing a generalised user behaviour model for next-POI recommendation. In: Proceedings of the 12th ACM Conference On Recommender Systems, pp 402\u2013406","DOI":"10.1145\/3240323.3240392"},{"key":"531_CR74","doi-asserted-by":"crossref","unstructured":"Wu Q, Zhang H, Gao X, He P, Weng P, Gao H, Chen G (2019) Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In: The World Wide Web conference, pp 2091\u20132102","DOI":"10.1145\/3308558.3313442"},{"key":"531_CR75","doi-asserted-by":"crossref","unstructured":"Massimo D, Ricci F (2023) Combining reinforcement learning and spatial proximity exploration for new user and new POI recommendations. In: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, pp 164\u2013174","DOI":"10.1145\/3565472.3592966"},{"issue":"1","key":"531_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3448099","volume":"5","author":"Y Guo","year":"2021","unstructured":"Guo Y, Liu F, Cai Z, Zeng H, Chen L, Zhou T, Xiao N (2021) PREFER: point-of-interest REcommendation with efficiency and privacy-preservation via Federated Edge leaRning. Proc ACM Interact Mob Wearable Ubiquitous Technol 5(1):1\u201325","journal-title":"Proc ACM Interact Mob Wearable Ubiquitous Technol"},{"key":"531_CR77","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/j.ins.2022.12.024","volume":"623","author":"V Perifanis","year":"2023","unstructured":"Perifanis V, Drosatos G, Stamatelatos G, Efraimidis PS (2023) FedPOIRec: privacy-preserving federated poi recommendation with social influence. Inf Sci 623:767\u2013790","journal-title":"Inf Sci"},{"key":"531_CR78","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 e5161. https:\/\/doi.org\/10.1002\/dac.5161","DOI":"10.1002\/dac.5161"},{"key":"531_CR79","doi-asserted-by":"crossref","unstructured":"Huang W, Liu J, Li T, Huang T, Ji S, Wan J (2021) Feddsr: daily schedule recommendation in a federated deep reinforcement learning framework. IEEE Trans Knowl Data Eng 35(4):3912-3924","DOI":"10.1109\/TKDE.2021.3130265"},{"key":"531_CR80","doi-asserted-by":"crossref","unstructured":"Wang LE, Wang Y, Bai Y, Liu P, Li X (2021) POI recommendation with federated learning and privacy preserving in cross domain recommendation. In: IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, pp 1\u20136","DOI":"10.1109\/INFOCOMWKSHPS51825.2021.9484510"},{"key":"531_CR81","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, pp 3462\u201334618","DOI":"10.1145\/3308558.3313609"},{"key":"531_CR82","doi-asserted-by":"crossref","unstructured":"He X, He Z, Du X, Chua TS (2018) Adversarial personalized ranking for recommendation. In: The 41st International ACM SIGIR Conference On Research & Development In Information Retrieval, pp 355\u2013364","DOI":"10.1145\/3209978.3209981"},{"key":"531_CR83","doi-asserted-by":"crossref","unstructured":"Li R, Wu X, Wang W (2020) Adversarial learning to compare: Self-attentive prospective customer recommendation in location based social networks. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp 349\u2013357","DOI":"10.1145\/3336191.3371841"},{"issue":"19","key":"531_CR84","doi-asserted-by":"publisher","DOI":"10.3390\/app10196664","volume":"10","author":"B Xia","year":"2020","unstructured":"Xia B, Bai Y, Yin J, Li Q, Xu L (2020) MTPR: a multi-task learning based poi recommendation considering temporal check-ins and geographical locations. Appl Sci 10(19):6664","journal-title":"Appl Sci"},{"key":"531_CR85","doi-asserted-by":"crossref","unstructured":"Liu W, Wang ZJ, Yao B, Yin J (2019) Geo-ALM: POI recommendation by fusing geographical information and adversarial learning mechanism. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI),  vol 7, pp 1807\u20131813","DOI":"10.24963\/ijcai.2019\/250"},{"key":"531_CR86","doi-asserted-by":"publisher","first-page":"2458","DOI":"10.1007\/s10489-017-1103-0","volume":"48","author":"S Xing","year":"2018","unstructured":"Xing S, Liu F, Zhao X, Li T (2018) Points-of-interest recommendation based on convolution matrix factorization. Appl Intell 48:2458\u20132469","journal-title":"Appl Intell"},{"key":"531_CR87","doi-asserted-by":"crossref","unstructured":"Tal O, Liu Y (2019) A joint deep recommendation framework for location\u2010based social networks. Complexity, 2019(1), 2926749","DOI":"10.1155\/2019\/2926749"},{"issue":"3","key":"531_CR88","doi-asserted-by":"publisher","DOI":"10.3390\/fi13030079","volume":"13","author":"S Safavi","year":"2021","unstructured":"Safavi S, Jalali M (2021) RecPOID: POI recommendation with friendship aware and deep CNN. Future Internet 13(3):79","journal-title":"Future Internet"},{"issue":"15","key":"531_CR89","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6981","volume":"34","author":"S Safavi","year":"2022","unstructured":"Safavi S, Jalali M (2022) DeePOF: a hybrid approach of deep convolutional neural network and friendship to point-of-interest (POI) recommendation system in location-based social networks. Concurrency Computation: Pract Experience 34(15):e6981","journal-title":"Concurrency Computation: Pract Experience"},{"key":"531_CR90","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2019.04.022","volume":"349","author":"PY Hao","year":"2019","unstructured":"Hao PY, Cheang WH, Chiang JH (2019) Real-time event embedding for POI recommendation. Neurocomputing 349:1\u201311","journal-title":"Neurocomputing"},{"issue":"6","key":"531_CR91","first-page":"1761","volume":"21","author":"X Lu","year":"2020","unstructured":"Lu X, Zhang H (2020) A content-aware POI recommendation method in location-based Social Networks based on deep CNN and Multi-objective Immune optimization. J Internet Technol 21(6):1761\u20131772","journal-title":"J Internet Technol"},{"issue":"4","key":"531_CR92","doi-asserted-by":"publisher","first-page":"495","DOI":"10.3390\/bioengineering10040495","volume":"10","author":"AB Kasgari","year":"2023","unstructured":"Kasgari AB, Safavi S, Nouri M, Hou J, Sarshar NT, Ranjbarzadeh R (2023) Point-of-interest preference model using an attention mechanism in a convolutional neural network. Bioengineering 10(4):495","journal-title":"Bioengineering"},{"key":"531_CR93","doi-asserted-by":"publisher","first-page":"156804","DOI":"10.1109\/ACCESS.2019.2950061","volume":"7","author":"Y Xu","year":"2019","unstructured":"Xu Y, Li X, Li J, Wang C, Gao R, Yu Y (2019) Ssser: spatiotemporal sequential and social embedding rank for successive point-of-interest recommendation. IEEE Access 7:156804\u2013156823","journal-title":"IEEE Access"},{"issue":"6","key":"531_CR94","doi-asserted-by":"publisher","DOI":"10.3390\/su15065034","volume":"15","author":"X Fan","year":"2023","unstructured":"Fan X, Hua Y, Cao Y, Zhao X (2023) Capturing dynamic interests of similar users for POI recommendation using self-attention mechanism. Sustainability 15(6):5034","journal-title":"Sustainability"},{"issue":"1","key":"531_CR95","first-page":"100161","volume":"3","author":"M Acharya","year":"2023","unstructured":"Acharya M, Yadav S, Mohbey KK (2023) How can we create a recommender system for tourism? A location centric spatial binning-based methodology using social networks. Int J Inform Manage Data Insights 3(1):100161","journal-title":"Int J Inform Manage Data Insights"},{"key":"531_CR96","unstructured":"Palumbo E, Rizzo G, Troncy R, Baralis E (2017) Predicting your next stop-over from location-based social network data with recurrent neural networks. In RecTour@ RecSys, pp 1\u20138"},{"issue":"8","key":"531_CR97","doi-asserted-by":"publisher","first-page":"355","DOI":"10.3390\/ijgi8080355","volume":"8","author":"C Liu","year":"2019","unstructured":"Liu C, Liu J, Wang J, Xu S, Han H, Chen Y (2019) An attention-based spatiotemporal gated recurrent unit network for point-of-interest recommendation. ISPRS Int J Geo-Information 8(8):355","journal-title":"ISPRS Int J Geo-Information"},{"key":"531_CR98","doi-asserted-by":"crossref","unstructured":"Lim N, Hooi B, Ng SK, Wang X, Goh YL, Weng R, Varadarajan J (2020) STP-UDGAT: Spatial-temporal-preference user dimensional graph attention network for next POI recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp 845\u2013854","DOI":"10.1145\/3340531.3411876"},{"key":"531_CR99","doi-asserted-by":"crossref","unstructured":"Zhao P, Luo A, Liu Y, Xu J, Li Z, Zhuang F, \u2026 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","DOI":"10.1109\/TKDE.2020.3007194"},{"issue":"4","key":"531_CR100","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1109\/TCYB.2020.3000733","volume":"52","author":"YC Chen","year":"2020","unstructured":"Chen YC, Thaipisutikul T, Shih TK (2020) A learning-based POI recommendation with spatiotemporal context awareness. IEEE Trans Cybern 52(4):2453\u20132466","journal-title":"IEEE Trans Cybern"},{"key":"531_CR101","doi-asserted-by":"publisher","unstructured":"Chang B, Park Y, Park D, Kim S, Kang J (2018) Content-aware hierarchical point-of-interest embedding model for successive POI recommendation. https:\/\/doi.org\/10.24963\/ijcai.2018\/458","DOI":"10.24963\/ijcai.2018\/458"},{"issue":"1","key":"531_CR102","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":"531_CR103","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: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp 2184\u20132190","DOI":"10.24963\/ijcai.2020\/302"},{"issue":"1","key":"531_CR104","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/s13278-023-01106-8","volume":"13","author":"M Acharya","year":"2023","unstructured":"Acharya M, Mohbey KK (2023) Trust-aware spatial-temporal feature estimation for next POI recommendation in location-based social networks. Social Netw Anal Min 13(1):102","journal-title":"Social Netw Anal Min"},{"key":"531_CR105","doi-asserted-by":"crossref","unstructured":"Cui Q, Zhang C, Zhang Y, Wang J, Cai M (2021) St-pil: Spatial-temporal periodic interest learning for next point-of-interest recommendation. In: Proceedings of the 30th ACM International Conference On Information & Knowledge Management, pp 2960\u20132964","DOI":"10.1145\/3459637.3482189"},{"key":"531_CR106","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1007\/s11280-018-0599-5","volume":"22","author":"YS Lu","year":"2019","unstructured":"Lu YS, Shih WY, Gau HY, Chung KC, Huang JL (2019) On successive point-of-interest recommendation. World Wide Web 22:1151\u20131173","journal-title":"World Wide Web"},{"key":"531_CR107","doi-asserted-by":"crossref","unstructured":"Liu Y, Wu AB (2021) POI recommendation method using deep learning in location-based social networks.\u00a0Wirel Commun Mob Comput 2021(1):9120864","DOI":"10.1155\/2021\/9120864"},{"key":"531_CR108","doi-asserted-by":"crossref","unstructured":"Li R, Shen Y, Zhu Y (2018) Next point-of-interest recommendation with temporal and multi-level context attention. In: 2018 IEEE International Conference on Data Mining (ICDM), IEEE, pp 1110\u20131115","DOI":"10.1109\/ICDM.2018.00144"},{"key":"531_CR109","doi-asserted-by":"crossref","unstructured":"Ma C, Zhang Y, Wang Q, Liu X (2018) Point-of-interest recommendation: Exploiting self-attentive autoencoders with neighbor-aware influence. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp 697\u2013706","DOI":"10.1145\/3269206.3271733"},{"issue":"10","key":"531_CR110","doi-asserted-by":"publisher","first-page":"433","DOI":"10.3390\/ijgi8100433","volume":"8","author":"J Huang","year":"2019","unstructured":"Huang J, Liu Y, Chen Y, Jia C (2019) Dynamic recommendation of POI sequence responding to historical trajectory. ISPRS Int J Geo-Information 8(10):433","journal-title":"ISPRS Int J Geo-Information"},{"key":"531_CR111","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1007\/s11280-020-00793-z","volume":"23","author":"Y Pan","year":"2020","unstructured":"Pan Y, He F, Yu H (2020) Learning social representations with deep autoencoder for recommender system. World Wide Web 23:2259\u20132279","journal-title":"World Wide Web"},{"key":"531_CR112","doi-asserted-by":"crossref","unstructured":"Wang X, Fukumoto F, Cui J, Suzuki Y, Li J, Yu D (2023) EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 383\u2013392","DOI":"10.1145\/3539618.3591678"},{"key":"531_CR113","doi-asserted-by":"crossref","unstructured":"Yuan Q, Cong G, Sun A (2014) Graph-based point-of-interest recommendation with geographical and temporal influences. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, pp 659\u2013668","DOI":"10.1145\/2661829.2661983"},{"key":"531_CR114","doi-asserted-by":"crossref","unstructured":"Yang C, Bai L, Zhang C, Yuan Q, Han J (2017) Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1245\u20131254","DOI":"10.1145\/3097983.3098094"},{"key":"531_CR115","doi-asserted-by":"publisher","first-page":"171105","DOI":"10.1109\/ACCESS.2019.2956138","volume":"7","author":"K Yang","year":"2019","unstructured":"Yang K, Zhu J (2019) Next poi recommendation via graph embedding representation from h-deepwalk on hybrid network. IEEE Access 7:171105\u2013171113","journal-title":"IEEE Access"},{"issue":"18","key":"531_CR116","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11182966","volume":"11","author":"S Zhang","year":"2022","unstructured":"Zhang S, Bai Z, Li P, Chang Y (2022) Multi-graph convolutional network for fine-grained and personalized POI recommendation. Electronics 11(18):2966","journal-title":"Electronics"},{"key":"531_CR117","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/s41019-020-00135-z","volume":"5","author":"S Wu","year":"2020","unstructured":"Wu S, Zhang Y, Gao C, Bian K, Cui B (2020) Garg: anonymous recommendation of point-of-interest in mobile networks by graph convolution network. Data Sci Eng 5:433\u2013447","journal-title":"Data Sci Eng"},{"key":"531_CR118","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.future.2019.07.074","volume":"102","author":"YS Lu","year":"2020","unstructured":"Lu YS, Huang JL (2020) GLR: a graph-based latent representation model for successive POI recommendation. Future Generation Comput Syst 102:230\u2013244","journal-title":"Future Generation Comput Syst"},{"key":"531_CR119","doi-asserted-by":"crossref","unstructured":"Wang D, Chen C, Di C, Shu M (2023) Exploring behavior patterns for next-POI recommendation via graph self-supervised learning. Electronics 12(8):1939","DOI":"10.3390\/electronics12081939"},{"key":"531_CR120","doi-asserted-by":"crossref","unstructured":"Xu W, Yi H, Song J, Li X (2023) Point of interest recommendation method based on bidirectional matrix and deep belief network. IEEE Trans Electr Electron Eng 18(6):923-931","DOI":"10.1002\/tee.23805"},{"issue":"11","key":"531_CR121","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1109\/TKDE.2017.2741484","volume":"29","author":"H Yin","year":"2017","unstructured":"Yin H, Wang W, Wang H, Chen L, Zhou X (2017) Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Trans Knowl Data Eng 29(11):2537\u20132551","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"531_CR122","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3474838","volume":"13","author":"N Gao","year":"2022","unstructured":"Gao N, Xue H, Shao W, Zhao S, Qin KK, Prabowo A, Rahaman MS, Salim FD (2022) Generative adversarial networks for spatio-temporal data: a survey. ACM Trans Intell Syst Technol 13(2):1\u201325. https:\/\/doi.org\/10.1145\/3474838","journal-title":"ACM Trans Intell Syst Technol"},{"key":"531_CR123","doi-asserted-by":"crossref","unstructured":"Shi M, Shen D, Kou Y, Nie T, Yu G (2021) Missing POI check-in identification using generative adversarial networks. In: Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11\u201314, 2021, Proceedings, Part I 26, Springer International Publishing, pp 575\u2013590","DOI":"10.1007\/978-3-030-73194-6_38"},{"key":"531_CR124","doi-asserted-by":"crossref","unstructured":"Gao Q, Trajcevski G, Zhou F, Zhang K, Zhong T, Zhang F (2019) DeepTrip: Adversarially understanding human mobility for trip recommendation. In: Proceedings of the 27th ACM SIGSPATIAL International Conference On Advances In Geographic Information Systems, pp 444\u2013447","DOI":"10.1145\/3347146.3359088"},{"issue":"1","key":"531_CR125","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/s13278-023-01076-x","volume":"13","author":"S Huang","year":"2023","unstructured":"Huang S, Wu X, Wu X, Wang K (2023) Sentiment analysis algorithm using contrastive learning and adversarial training for POI recommendation. Social Netw Anal Min 13(1):75","journal-title":"Social Netw Anal Min"},{"key":"531_CR126","doi-asserted-by":"crossref","unstructured":"Gong L, Lin Y, Guo S, Lin Y, Wang T, Zheng E, \u2026 Wan H (2023) Contrastive pre-training with adversarial perturbations for check-in sequence representation learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 37, no 4, pp 4276\u20134283","DOI":"10.1609\/aaai.v37i4.25546"},{"issue":"1","key":"531_CR127","first-page":"1","volume":"13","author":"F Zhou","year":"2021","unstructured":"Zhou F, Wang P, Xu X, Tai W, Trajcevski G (2021) Contrastive trajectory learning for tour recommendation. ACM Trans Intell Syst Technol (TIST) 13(1):1\u201325","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"531_CR128","doi-asserted-by":"crossref","unstructured":"Li Y, Fan Z, Yin D, Jiang R, Deng J, Song X (2022) HMGCL: heterogeneous multigraph contrastive learning for LBSN friend recommendation. World Wide Web, pp 1\u201324","DOI":"10.1007\/s11280-022-01092-5"},{"issue":"6","key":"531_CR129","doi-asserted-by":"publisher","first-page":"7351","DOI":"10.1007\/s11063-023-11264-7","volume":"55","author":"Q Dong","year":"2023","unstructured":"Dong Q, Liu B, Zhang X, Qin J, Wang B (2023) Sequential POI Recommend Based on Personalized Federated Learning. Neural Process Lett 55(6):7351\u20137368","journal-title":"Neural Process Lett"},{"key":"531_CR130","doi-asserted-by":"publisher","unstructured":"Zhang JD, Ghinita G, Chow CY (2014) Differentially private location recommendations in geosocial networks. In: 2014 IEEE 15th International Conference on Mobile Data Management, Brisbane, QLD, Australia, pp 59\u201368. https:\/\/doi.org\/10.1109\/MDM.2014.13","DOI":"10.1109\/MDM.2014.13"},{"issue":"3","key":"531_CR131","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1007\/s42979-023-01683-7","volume":"4","author":"M Acharya","year":"2023","unstructured":"Acharya M, Mohbey KK (2023) Differential privacy-based Social Network Detection over spatio-temporal proximity for secure POI recommendation. SN Comput Sci 4(3):252","journal-title":"SN Comput Sci"},{"key":"531_CR132","doi-asserted-by":"crossref","unstructured":"Zhang JD, Chow CY, Li Y (2014) Lore: Exploiting sequential influence for location recommendations. In: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp 103\u2013112","DOI":"10.1145\/2666310.2666400"},{"key":"531_CR133","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.neucom.2017.02.067","volume":"242","author":"Y Ying","year":"2017","unstructured":"Ying Y, Chen L, Chen G (2017) A temporal-aware POI recommendation system using context-aware tensor decomposition and weighted HITS. Neurocomputing 242:195\u2013205","journal-title":"Neurocomputing"},{"key":"531_CR134","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s10115-015-0825-8","volume":"46","author":"C Zhang","year":"2016","unstructured":"Zhang C, Wang K (2016) POI recommendation through cross-region collaborative filtering. Knowl Inf Syst 46:369\u2013387","journal-title":"Knowl Inf Syst"},{"key":"531_CR135","unstructured":"S\u00e1nchez P, Bellog\u00edn A (2018) A novel approach for venue recommendation using cross-domain techniques. arXiv preprint arXiv:1809.09864"},{"issue":"8","key":"531_CR136","doi-asserted-by":"publisher","first-page":"3536","DOI":"10.1109\/TKDE.2020.3033841","volume":"34","author":"D Li","year":"2020","unstructured":"Li D, Gong Z (2020) A deep neural network for crossing-city poi recommendations. IEEE Trans Knowl Data Eng 34(8):3536\u20133548","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"12","key":"531_CR137","doi-asserted-by":"publisher","first-page":"4282","DOI":"10.1109\/TCYB.2018.2861897","volume":"49","author":"D Li","year":"2018","unstructured":"Li D, Gong Z, Zhang D (2018) A common topic transfer learning model for crossing city POI recommendations. IEEE Trans Cybern 49(12):4282\u20134295","journal-title":"IEEE Trans Cybern"},{"key":"531_CR138","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s10619-020-07299-7","volume":"38","author":"H Zhang","year":"2020","unstructured":"Zhang H, Wei S, Hu X, Li Y, Xu J (2020) On accurate POI recommendation via transfer learning. Distrib Parallel Databases 38:585\u2013599","journal-title":"Distrib Parallel Databases"},{"key":"531_CR139","doi-asserted-by":"crossref","unstructured":"Yang T, Gao Y, Huang Z, Liu Y (2023) UPTDNet: A user preference transfer and drift network for cross-city next POI recommendation. Int J Intell Syst 2023:9091570","DOI":"10.1155\/2023\/9091570"},{"key":"531_CR140","doi-asserted-by":"publisher","first-page":"91027","DOI":"10.1109\/ACCESS.2021.3091426","volume":"9","author":"TB Ogunseyi","year":"2021","unstructured":"Ogunseyi TB, Avoussoukpo CB, Jiang Y (2021) Privacy-preserving matrix factorization for cross-domain recommendation. IEEE Access 9:91027\u201391037","journal-title":"IEEE Access"},{"key":"531_CR141","doi-asserted-by":"crossref","unstructured":"Han L, Luo W, Yang A, Cheng Y, Lu R, Lai J, Zheng Y (2023) Ad hocprivacy-preserving cross-domain point-of-interests recommendation based on friendship in Lbss. Available at SSRN 4356599","DOI":"10.2139\/ssrn.4356599"},{"key":"531_CR142","doi-asserted-by":"crossref","unstructured":"Purushotham S, Kuo CCJ, Shahabdeen J, Nachman L (2014) Collaborative group-activity recommendation in location-based social networks. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, pp 8\u201315","DOI":"10.1145\/2676440.2676442"},{"key":"531_CR143","doi-asserted-by":"crossref","unstructured":"Ravi L, Vairavasundaram S (2016) A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. Comput Intell Neurosci 2016:1291358","DOI":"10.1155\/2016\/1291358"},{"key":"531_CR144","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.procs.2017.09.020","volume":"114","author":"RD Gottapu","year":"2017","unstructured":"Gottapu RD, Monangi LVS (2017) Point-of-interest recommender system for social groups. Procedia Comput Sci 114:159\u2013164","journal-title":"Procedia Comput Sci"},{"key":"531_CR145","doi-asserted-by":"crossref","unstructured":"Schiaffino S, Godoy D, Pace JAD, Demazeau Y (2020) A MAS-based approach for POI group recommendation in LBSN. In: Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection: 18th International Conference, PAAMS 2020, L\u2019Aquila, Italy, October 7\u20139, 2020, Proceedings 18, Springer International Publishing, pp 238\u2013250","DOI":"10.1007\/978-3-030-49778-1_19"},{"key":"531_CR146","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114593","volume":"171","author":"ZB Sojahrood","year":"2021","unstructured":"Sojahrood ZB, Taleai M (2021) A POI group recommendation method in location-based social networks based on user influence. Expert Syst Appl 171:114593","journal-title":"Expert Syst Appl"},{"issue":"3","key":"531_CR147","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.icte.2020.05.005","volume":"6","author":"X Wang","year":"2020","unstructured":"Wang X, Nguyen M, Carr J, Cui L, Lim K (2020) A group preference-based privacy-preserving POI recommender system. ICT Express 6(3):204\u2013208","journal-title":"ICT Express"},{"key":"531_CR148","doi-asserted-by":"crossref","unstructured":"Zhang J, Gao M, Yu J, Guo L, Li J, Yin H (2021) Double-scale self-supervised hypergraph learning for group recommendation. In Proceedings of the 30th ACM International Conference On Information & Knowledge Management, pp 2557\u20132567","DOI":"10.1145\/3459637.3482426"},{"key":"531_CR149","doi-asserted-by":"crossref","unstructured":"Li H, Ge Y, Hong R, Zhu H (2016) Point-of-interest recommendations: Learning potential check-ins from friends. In: Proceedings of the 22nd ACM SIGKDD International Conference On Knowledge Discovery And Data Mining, pp 975\u2013984","DOI":"10.1145\/2939672.2939767"},{"issue":"5","key":"531_CR150","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"},{"issue":"21","key":"531_CR151","doi-asserted-by":"publisher","DOI":"10.3390\/math9212673","volume":"9","author":"C Xu","year":"2021","unstructured":"Xu C, Liu D, Mei X (2021) Exploring an efficient POI recommendation model based on user characteristics and spatial-temporal factors. Mathematics 9(21):2673","journal-title":"Mathematics"},{"key":"531_CR152","doi-asserted-by":"publisher","first-page":"47973","DOI":"10.1109\/ACCESS.2020.2979922","volume":"8","author":"K Cao","year":"2020","unstructured":"Cao K, Guo J, Meng G, Liu H, Liu Y, Li G (2020) Points-of-interest recommendation algorithm based on LBSN in edge computing environment. IEEE Access 8:47973\u201347983","journal-title":"IEEE Access"},{"key":"531_CR153","doi-asserted-by":"publisher","first-page":"117700","DOI":"10.1016\/j.eswa.2022.117700","volume":"205","author":"HA Rahmani","year":"2022","unstructured":"Rahmani HA, Deldjoo Y, Di Noia T (2022) The role of context fusion on accuracy, beyond-accuracy, and fairness of point-of-interest recommendation systems. Expert Syst Appl 205:117700","journal-title":"Expert Syst Appl"},{"key":"531_CR154","doi-asserted-by":"crossref","unstructured":"Feng S, Cong G, An B, Chee YM (2017) Poi2vec: Geographical latent representation for predicting future visitors. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 31, no 1","DOI":"10.1609\/aaai.v31i1.10500"},{"key":"531_CR155","doi-asserted-by":"crossref","unstructured":"Saleem MA, Costa D, Dolog FS, Karras P, Pedersen P, T. B., Calders T (2018) Predicting visitors using location-based social networks. In: 2018 19th IEEE International Conference on Mobile Data Management (MDM), IEEE, pp 245\u2013250","DOI":"10.1109\/MDM.2018.00043"},{"key":"531_CR156","doi-asserted-by":"crossref","unstructured":"Lou P, Zhao G, Qian X, Wang H, Hou X (2016) Schedule a rich sentimental travel via sentimental POI mining and recommendation. In: 2016 IEEE Second International Conference On Multimedia Big Data (BigMM), IEEE, pp 33\u201340","DOI":"10.1109\/BigMM.2016.38"},{"key":"531_CR157","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.jnca.2016.12.033","volume":"82","author":"L Zhu","year":"2017","unstructured":"Zhu L, Xu C, Guan J, Zhang H (2017) SEM-PPA: a semantical pattern and preference-aware service mining method for personalized point of interest recommendation. J Netw Comput Appl 82:35\u201346","journal-title":"J Netw Comput Appl"},{"key":"531_CR158","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.proenv.2014.11.025","volume":"22","author":"P Aksenov","year":"2014","unstructured":"Aksenov P, Kemperman A, Arentze T (2014) Toward personalised and dynamic cultural routing: a three-level approach. Procedia Environ Sci 22:257\u2013269","journal-title":"Procedia Environ Sci"},{"issue":"3","key":"531_CR159","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1016\/j.tourman.2007.06.004","volume":"29","author":"C De Rojas","year":"2008","unstructured":"De Rojas C, Camarero C (2008) Visitors\u2019 experience, mood and satisfaction in a heritage context: evidence from an interpretation center. Tour Manag 29(3):525\u2013537","journal-title":"Tour Manag"},{"issue":"11","key":"531_CR160","first-page":"1","volume":"30","author":"L Liu","year":"2022","unstructured":"Liu L, Luhach AK, Lee MH (2022) POIs Category recommendation for cultural country travel enterprises based on check-in information. J Global Inform Manage (JGIM) 30(11):1\u201315","journal-title":"J Global Inform Manage (JGIM)"},{"key":"531_CR161","doi-asserted-by":"crossref","unstructured":"Dietz LW, Roy R, W\u00f6rndl W (2019) Characterisation of traveller types using check-in data from location-based social networks. In: Information and Communication Technologies in Tourism 2019: Proceedings of the International Conference in Nicosia, Cyprus, January 30\u2013February 1, 2019, Springer International Publishing, pp 15\u201326","DOI":"10.1007\/978-3-030-05940-8_2"},{"key":"531_CR162","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/s10618-018-0588-4","volume":"33","author":"TN Doan","year":"2019","unstructured":"Doan TN, Lim EP (2019) Modeling location-based social network data with area attraction and neighborhood competition. Data Min Knowl Disc 33:58\u201395","journal-title":"Data Min Knowl Disc"},{"key":"531_CR163","doi-asserted-by":"crossref","unstructured":"Yang D, Zhang D, Yu Z, Wang Z (2013), May A sentiment-enhanced personalized location recommendation system. In: Proceedings of the 24th ACM conference on hypertext and social media, pp 119\u2013128","DOI":"10.1145\/2481492.2481505"},{"key":"531_CR164","doi-asserted-by":"crossref","unstructured":"Yang D, Zhang D, Yu Z, Yu Z  (2013) Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp 479\u2013488","DOI":"10.1145\/2493432.2493464"},{"key":"531_CR165","doi-asserted-by":"crossref","unstructured":"Papalexakis E, Pelechrinis K, Faloutsos C (2014) Spotting misbehaviors in location-based social networks using tensors. In: Proceedings of the 23rd International Conference on World Wide Web, pp 551\u2013552","DOI":"10.1145\/2567948.2576950"},{"issue":"6","key":"531_CR166","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.1007\/s10618-022-00865-w","volume":"36","author":"S Halder","year":"2022","unstructured":"Halder S, Lim KH, Chan J, Zhang X (2022) POI recommendation with queuing time and user interest awareness. Data Min Knowl Disc 36(6):2379\u20132409","journal-title":"Data Min Knowl Disc"},{"key":"531_CR167","doi-asserted-by":"crossref","unstructured":"Safavi S, Jalali M, Houshmand M (2022) Toward point-of-interest recommendation systems: A critical review on deep-learning Approaches. Electronics 11(13):1998","DOI":"10.3390\/electronics11131998"},{"key":"531_CR168","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114018","volume":"164","author":"YD Seo","year":"2021","unstructured":"Seo YD, Cho YS (2021) Point of interest recommendations based on the anchoring effect in location-based social network services. Expert Syst Appl 164:114018","journal-title":"Expert Syst Appl"}],"container-title":["GeoInformatica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-024-00531-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10707-024-00531-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-024-00531-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T01:16:26Z","timestamp":1757121386000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10707-024-00531-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":168,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["531"],"URL":"https:\/\/doi.org\/10.1007\/s10707-024-00531-x","relation":{},"ISSN":["1384-6175","1573-7624"],"issn-type":[{"value":"1384-6175","type":"print"},{"value":"1573-7624","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"22 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}