{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:10:17Z","timestamp":1774447817869,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Location-based social networks (LBSNs) leverage geo-location technologies to connect users with places, events, and other users nearby. Using GPS data, platforms like Foursquare enable users to check into locations, share their locations, and receive location-based recommendations. A significant research gap in LBSNs lies in the limited exploration of users\u2019 tendencies to withhold certain location data. While existing studies primarily focus on the locations users choose to disclose and the activities they attend, there is a lack of research on the hidden or intentionally omitted locations. Understanding these concealed patterns and integrating them into predictive models could enhance the accuracy and depth of location prediction, offering a more comprehensive view of user mobility behavior. This paper solves this gap by proposing an Associative Hidden Location Trajectory Prediction model (AHLTP) that leverages user trajectories to infer unchecked locations. The FP-growth mining technique is used in AHLTP to extract frequent patterns of check-in locations, combined with machine-learning methods such as K-nearest-neighbor, gradient-boosted-trees, and deep learning to classify hidden locations. Moreover, AHLTP uses association rule mining to derive the frequency of successive check-in pairs for the purpose of hidden location prediction. The proposed AHLTP integrated with the machine-learning models classifies the data effectively, with the KNN attaining the highest accuracy at 98%, followed by gradient-boosted trees at 96% and deep learning at 92%. Comparative study using a real-world dataset demonstrates the model\u2019s superior accuracy compared to state-of-the-art approaches.<\/jats:p>","DOI":"10.3390\/bdcc9040102","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:48:46Z","timestamp":1744850926000},"page":"102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Efficient Trajectory Prediction Using Check-In Patterns in Location-Based Social Network"],"prefix":"10.3390","volume":"9","author":[{"given":"Eman M.","family":"Bahgat","sequence":"first","affiliation":[{"name":"Faculty of Computer and Information Science, Ain Shams University, Cairo 11241, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0609-2300","authenticated-orcid":false,"given":"Alshaimaa","family":"Abo-alian","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Science, Ain Shams University, Cairo 11241, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4991-966X","authenticated-orcid":false,"given":"Sherine","family":"Rady","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Science, Ain Shams University, Cairo 11241, Egypt"}]},{"given":"Tarek F.","family":"Gharib","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Science, Ain Shams University, Cairo 11241, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s42044-019-00052-z","article-title":"A Comprehensive Survey on Trajectory-Based Location Prediction","volume":"3","author":"Koolwal","year":"2020","journal-title":"Iran J. Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Khan, N.U., Wan, W., and Yu, S. (2020). Location-Based Social Network\u2019s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9020076"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Muhammad, R., Zhao, Y., and Liu, F. (2019). Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China. Sustainability, 11.","DOI":"10.3390\/su11102822"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bahgat, E.M., Rady, S., Abo-Alian, A., and Gharib, T.F. (2023, January 21\u201323). A Comparative Study on Point-of-Interest Recommendation Techniques in Location-Based Social Network. Proceedings of the Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/ICICIS58388.2023.10391195"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kim, J.S., Jin, H., Kavak, H., Rouly, O.C., Crooks, A., Pfoser, D., Wenk, C., and Z\u00fcfle, A. (July, January 30). Location-based social network data generation based on patterns of life. Proceedings of the 2020 21st IEEE International Conference on Mobile Data Management (MDM), Versailles, France.","DOI":"10.1109\/MDM48529.2020.00038"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s10707-014-0220-8","article-title":"Recommendations in Location-Based Social Networks: A Survey","volume":"19","author":"Bao","year":"2015","journal-title":"Geoinformatica"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"109721","DOI":"10.1016\/j.engappai.2024.109721","article-title":"The Multi-Module Joint Modeling Approach: Predicting Urban Crowd Flow by Integrating Spatial\u2013Temporal Patterns and Dynamic Periodic Relationship","volume":"141","author":"Abideen","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_8","first-page":"119","article-title":"A Location Prediction Methods: State of art","volume":"21","author":"Gad","year":"2021","journal-title":"Int. J. Intell. Comput. Inf. Sci."},{"key":"ref_9","unstructured":"Nezhadettehad, A., Zaslavsky, A., Abdur, R., Shaikh, S.A., Loke, S.W., Huang, G.-L., and Hassani, A. (2024). Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1145\/2483669.2483682","article-title":"Semantic Trajectories: Mobility Data Computation and Annotation","volume":"4","author":"Yan","year":"2013","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1145\/3411827","article-title":"Extending Coverage of Stationary Sensing Systems with Mobile Sensing Systems for Human Mobility Modeling","volume":"4","author":"Yang","year":"2020","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_12","unstructured":"Chen, W., Liang, Y., Zhu, Y., Chang, Y., Luo, K., Wen, H., Li, L., Yu, Y., Wen, Q., and Chen, C. (2024). Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Scellato, S., Noulas, A., and Mascolo, C. (2011, January 21\u201324). Exploiting place features in link prediction on 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\/2020408.2020575"},{"key":"ref_14","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":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.knosys.2018.08.031","article-title":"An Adaptive Point-of-Interest Recommendation Method for Location-Based Social Networks Based on User Activity and Spatial Features","volume":"163","author":"Si","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105849","DOI":"10.1016\/j.knosys.2020.105849","article-title":"Personalized Location Recommendation by Fusing Sentimental and Spatial Context","volume":"196","author":"Zhao","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119048","DOI":"10.1016\/j.eswa.2022.119048","article-title":"Trust-Aware Location Recommendation in Location-Based Social Networks: A Graph-Based Approach","volume":"213","author":"Canturk","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, J., Yi, H., Gao, Y., and Jing, R. (2023). Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network. Electronics, 12.","DOI":"10.3390\/electronics12163495"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"114018","DOI":"10.1016\/j.eswa.2020.114018","article-title":"Point of Interest Recommendations Based on the Anchoring Effect in Location-Based Social Network Services","volume":"164","author":"Seo","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1080\/13658816.2024.2427258","article-title":"Exploring Human Mobility: A Time-Informed Approach to Pattern Mining and Sequence Similarity","volume":"39","author":"Yang","year":"2025","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Khan, N.U., Wan, W., Yu, S., Muzahid, A.A.M., Khan, S., and Hou, L. (2020). A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9120733"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s42486-023-00133-w","article-title":"Large-Scale Trajectory Prediction via Relationship-Aware Adaptive Hierarchical Graph Learning","volume":"5","author":"Yan","year":"2023","journal-title":"CCF Trans. Pervasive Comput. Interact."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, D., Fankhauser, B., Rosso, P., and Cudr\u2019e-Mauroux, P. (2020, January 11\u201317). Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States!. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Main track, Yokohama, Japan.","DOI":"10.24963\/ijcai.2020\/302"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Khan, N.U., Wan, W., Riaz, R., Jiang, S., and Wang, X. (2023). Prediction and Classification of User Activities Using Machine Learning Models from Location-Based Social Network Data. Appl. Sci., 13.","DOI":"10.3390\/app13063517"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1109\/JSYST.2019.2913080","article-title":"Venue2Vec: An Efficient Embedding Model for Fine-Grained User Location Prediction in Geo-Social Networks","volume":"14","author":"Xu","year":"2020","journal-title":"IEEE Syst. J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shao, J., Wang, Y., Gao, H., Shen, H., Li, Y., and Cheng, X. (2021, January 1\u20135). Locate who you are: Matching geo-location to text for user identity linkage. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Virtual Event.","DOI":"10.1145\/3459637.3482134"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109630","DOI":"10.1016\/j.engappai.2024.109630","article-title":"Time-Aware Cross-Domain Point-of-Interest Recommendation in Social Networks","volume":"139","author":"Acharya","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1007\/s10115-018-1170-5","article-title":"Hidden Location Prediction Using Check-in Patterns in Location-Based Social Networks","volume":"57","author":"Mazumdar","year":"2018","journal-title":"Knowl. Inf. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jnca.2018.02.007","article-title":"Effective Fine-Grained Location Prediction Based on User Check-in Pattern in LBSNs","volume":"108","author":"Cao","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ghanaati, F., Ekbatanifard, G., and Khoshhal, K. (2023). Using a Flexible Model to Compare the Efficacy of Geographical and Temporal Contextual Information of Location-Based Social Network Data for Location Prediction. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12040137"},{"key":"ref_31","first-page":"44","article-title":"A Novel Approach for Hiding Sensitive Association Rules Using DPQR Strategy in Recommendation Systems","volume":"20","author":"Kamal","year":"2020","journal-title":"Int. J. Intell. Comput. Inf. Sci."},{"key":"ref_32","first-page":"28","article-title":"Enhancing Machine Learning Engineering for Predicting Youth Loyalty in Digital Banking Using a Hybrid Meta-Learners","volume":"24","author":"Galal","year":"2024","journal-title":"Int. J. Intell. Comput. Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4663","DOI":"10.1109\/TKDE.2021.3049250","article-title":"Challenges in KNN Classification","volume":"34","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_34","unstructured":"(2024, December 12). Forsquare Dataset. Available online: https:\/\/foursquare.com."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/102\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:16:01Z","timestamp":1760030161000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,17]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["bdcc9040102"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9040102","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,17]]}}}