{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:34:05Z","timestamp":1767652445681,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In recent years, next location prediction has been of paramount importance for a wide range of location-based social network (LBSN) services. The influence of geographical and temporal contextual information (GTCI) is crucial for analyzing individual behaviors for personalized point-of-interest (POI) recommendations. A number of studies have considered GTCI to improve the performance of POI prediction algorithms, but they have limitations. Moreover, reviewing the related literature revealed that no research has investigated and evaluated the GTCI of LBSN data for location prediction in the form presented in this study. Here, we extended the gated recurrent unit (GRU) model by adding additional attention gates to separately consider GTCI for location prediction based on LBSN data and introduced the extended attention GRU (EAGRU) model. Furthermore, we used the flexibility of the EAGRU architecture and developed it in four states to compare the efficacy of GTCI for location prediction for LBSN users. Real-world, large-scale datasets based on two LBSNs (Gowalla and Foursquare) were used for a complete review. The results revealed that the performance of the EAGRU model was higher than that of competitive baseline methods. In addition, the efficacy of the geographical CI was significantly higher than the temporal CI.<\/jats:p>","DOI":"10.3390\/ijgi12040137","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T04:37:28Z","timestamp":1679546248000},"page":"137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Using a Flexible Model to Compare the Efficacy of Geographical and Temporal Contextual Information of Location-Based Social Network Data for Location Prediction"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3332-0898","authenticated-orcid":false,"given":"Fatemeh","family":"Ghanaati","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran"}]},{"given":"Gholamhossein","family":"Ekbatanifard","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9633-3114","authenticated-orcid":false,"given":"Kamrad","family":"Khoshhal Roudposhti","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fan, X., Guo, L., Han, N., Wang, Y., Shi, J., and Yuan, Y. (2018, January 9\u201311). A Deep Learning Approach for Next Location Prediction. Proceedings of the 22th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD), Nanjing, China.","DOI":"10.1109\/CSCWD.2018.8465289"},{"key":"ref_2","unstructured":"Baral, R., Li, T., and Zhu, X. (2018). CAPS: Context Aware Personalized POI Sequence Recommender System. arXiv."},{"key":"ref_3","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_4","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_5","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s10115-020-01541-5","article-title":"RELINE: Point-of-Interest Recommendations using Multiple Network Embeddings","volume":"63","author":"Christoforidis","year":"2019","journal-title":"J. Knowl. Inf. Syst."},{"key":"ref_6","unstructured":"Yuan, Q., Cong, G., Ma, Z., Sun, A., and Magnenat-Thalmann, N. (August, January 28). Time-aware Point-of-interest Recommendation. Proceedings of the 36th ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1109\/JAS.2017.7510538","article-title":"Partition-based collaborative tensor factorization for POI recommendation","volume":"4","author":"Luan","year":"2017","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Maroulis, S., Boutsis, I., and Kalogeraki, V. (2016, January 5\u20138). Context-aware Point-of-Interest Recommendation Using Tensor Factorization. Proceedings of the IEEE International Conference on Big Data, Washington, DC, USA.","DOI":"10.1109\/BigData.2016.7840694"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zhou, F., Trajcevski, G., Zhang, K., Zhong, T., and Zhang, F. (2019, January 13\u201317). Predicting Human Mobility via Variational Attention. Proceedings of the World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3313610"},{"key":"ref_10","first-page":"1","article-title":"DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation","volume":"21","author":"Huang","year":"2020","journal-title":"ACM Trans. Internet Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Manotumruksa, J., Macdonald, C., and Ounis, I. (2018, January 8\u201312). A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation. Proceedings of the 41th International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA.","DOI":"10.1145\/3209978.3210042"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yao, D., Zhang, C., Huang, J., and Bi, J. (2017, January 6\u201310). SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories. Proceedings of the ACM Conference on Information and Knowledge Management, Singapore.","DOI":"10.1145\/3132847.3133056"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TCSS.2019.2907563","article-title":"LORI: A Learning-to-Rank-Based Integration Method of Location Recommendation","volume":"6","author":"Li","year":"2019","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_14","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 Geographical and Temporal Contexts. Proceedings of the Conference on Artificial Intelligence (AAAI), Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"185928","DOI":"10.1109\/ACCESS.2019.2961559","article-title":"A Hybrid Markov and LSTM Model for Indoor Location Prediction","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1007\/s11704-018-8011-2","article-title":"NEXT: A Neural Network Framework for Next POI Recommendation","volume":"14","author":"Zhang","year":"2017","journal-title":"Front. Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Q., Wu, S., Wang, D., Li, Z., and Wang, L. (2016, January 12\u201315). Context-aware Sequential Recommendation. Proceedings of the IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain.","DOI":"10.1109\/ICDM.2016.0135"},{"key":"ref_19","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 Attention Recurrent Networks. Proceedings of the International World Wide Web Conference Committee-IW3C2, Lyon, France.","DOI":"10.1145\/3178876.3186058"},{"key":"ref_20","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 Conference on Artificial Intelligence (AAAI), New York, NY, USA.","DOI":"10.1609\/aaai.v34i01.5353"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"21980","DOI":"10.1109\/ACCESS.2018.2827422","article-title":"Multi-Context Integrated Deep Neural Network Model for Next Location Prediction","volume":"6","author":"Liao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kala, K.U., and Nandhini, M. (2019). Context Category Specific sequence aware Point of Interest Recommender System with Multi Gated Recurrent Unit. J. Ambient Intell. Humaniz. Comput.","DOI":"10.1007\/s12652-019-01583-w"},{"key":"ref_23","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 International Conference on Knowledge Discovery and Data (SIGKDD), San Diego, CA, USA.","DOI":"10.1145\/2020408.2020579"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TSMC.2014.2327053","article-title":"Modeling User Activity Preference by Leveraging User Geographical Temporal Characteristics in LBSNs","volume":"45","author":"Yang","year":"2014","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhao, S., Zhao, T., King, I., and Lyu, M.R. (2017, January 3\u20137). Geo-Teaser: Geo-Temporal Sequential Embedding Rank for Point-of-interest Recommendation. Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia.","DOI":"10.1145\/3041021.3054138"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"143025","DOI":"10.1109\/ACCESS.2019.2941280","article-title":"Short-term traffic flow prediction method for urban road sections based on space\u2013time analysis and GRU","volume":"7","author":"Dai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Doan, K.D., Yang, G., and Reddy, C.K. (2019, January 14\u201317). An Attentive Spatio-Temporal Neural Model for Successive Point of Interest. Proceedings of the Springer Pacific-Asia Conference on Knowledge Discovery and Data Mining, Macau, China.","DOI":"10.1007\/978-3-030-16142-2_27"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4020","DOI":"10.1002\/int.22710","article-title":"Bidirectional GRU networks-based next POI category prediction for healthcare","volume":"37","author":"Liu","year":"2020","journal-title":"Int. J. Intell. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.neucom.2021.01.067","article-title":"LSI-LSTM: An attention-aware LSTM for real-time driving destination prediction by considering location semantics and location importance of trajectory points","volume":"440","author":"Gui","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, X., Li, L., Chen, X., Liu, J., and Wu, H. (2021, January 5\u201310). Time-aware user modeling with check-in time prediction for next POI recommendation. Proceedings of the IEEE International Conference on Web Services (ICWS), Chicago, IL, USA.","DOI":"10.1109\/ICWS53863.2021.00028"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.neucom.2020.03.080","article-title":"A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction","volume":"403","author":"Li","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2453","DOI":"10.1109\/TCYB.2020.3000733","article-title":"A learning-based POI recommendation with spatiotemporal context awareness","volume":"52","author":"Chen","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_34","first-page":"111","article-title":"Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey","volume":"1","author":"Bokde","year":"2014","journal-title":"Int. J. Adv. Found. Res. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gan, M., and Gao, L. (2019). Discovering Memory-Based Preferences for POI Recommendation in Location-Based Social Networks. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8060279"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.procs.2015.04.237","article-title":"Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey","volume":"49","author":"Bokde","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Manotumruksa, J., Macdonald, C., and Ounis, I. (2017, January 6\u201310). A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation. Proceedings of the ACM Conference on Information and Knowledge Management, Singapore.","DOI":"10.1145\/3132847.3133036"},{"key":"ref_38","unstructured":"Islam, M.A., Mohammad, M.M., Sarathi Das, S.S., and Eunus Ali, M. (2020). A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"12256","DOI":"10.1007\/s11227-021-03768-7","article-title":"An optimized hybrid deep learning model using ensemble learning approach for human walking activities recognition","volume":"77","author":"Semwal","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_40","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt Thieme, L. (2009, January 18\u201321). BPR: Bayesian Personalized Ranking from Implicit Feedback. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"171105","DOI":"10.1109\/ACCESS.2019.2956138","article-title":"Next POI Recommendation via Graph Embedding Representation From H-Deepwalk on Hybrid Network","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_42","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3th International Conference for Learning Representations, San Diego, CA, USA."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/4\/137\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:01:19Z","timestamp":1760122879000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/4\/137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,23]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["ijgi12040137"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12040137","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2023,3,23]]}}}