{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:13:41Z","timestamp":1760368421468,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T00:00:00Z","timestamp":1587686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC0803101","2016YFC0803108"],"award-info":[{"award-number":["2016YFC0803101","2016YFC0803108"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Research Fund of Chinese Academy of Surveying and Mapping","award":["AR1929"],"award-info":[{"award-number":["AR1929"]}]},{"name":"Project of integrated spatiotemporal public service platform (Phase I) for national and local communication in Xiangxi Autonomous Prefecture","award":["QT1906"],"award-info":[{"award-number":["QT1906"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>With the rapid development of location-based social networks (LBSNs), because human behaviors exhibit specific distribution patterns, personalized geo-social recommendation has played a significant role for LBSNs. In addition to user preference and social influence, geographical influence has also been widely researched in location recommendation. Kernel density estimation (KDE) is a key method in modeling geographical influence. However, most current studies based on KDE do not consider the problems of influence range and outliers on users\u2019 check-in behaviors. In this paper, we propose a method to exploit geographical and synthetic social influences (GeSSo) on location recommendation. GeSSo uses a kernel estimation approach with a quartic kernel function to model geographical influences, and two kinds of weighted distance are adopted to calculate bandwidth. Furthermore, we consider the social closeness and connections between friends, and a synthetic friend-based recommendation method is introduced to model social influences. Finally, we adopt a sum framework which combines user\u2019s preferences on a location with geographical and social influences. Extensive experiments are conducted on three real-life datasets. The results show that our method achieves superior performance compared to other advanced geo-social recommendation techniques.<\/jats:p>","DOI":"10.3390\/ijgi9040285","type":"journal-article","created":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T11:42:14Z","timestamp":1587728534000},"page":"285","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Exploiting Two-Dimensional Geographical and Synthetic Social Influences for Location Recommendation"],"prefix":"10.3390","volume":"9","author":[{"given":"Jiping","family":"Liu","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Chinese Academy of Surveying and Mapping, Beijing 100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiran","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agen","family":"Qiu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1080\/13658816.2017.1400550","article-title":"Integrating spatial and temporal contexts into a factorization model for POI recommendation","volume":"32","author":"Cai","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10707-017-0298-x","article-title":"Personalized location recommendation by aggregating multiple recommenders in diversity","volume":"21","author":"Lu","year":"2017","journal-title":"GeoInformatica"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3231933","article-title":"Personalized Context-Aware Point of Interest Recommendation","volume":"36","author":"Aliannejadi","year":"2018","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.14778\/3115404.3115407","article-title":"An experimental evaluation of point-of-interest recommendation in location-based social networks","volume":"10","author":"Liu","year":"2017","journal-title":"Proc. VLDB Endow."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s11276-016-1316-x","article-title":"Mining user preferences of new locations on location-based social networks: A multidimensional cloud model approach","volume":"24","author":"Wang","year":"2018","journal-title":"Wirel. Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ogundele, T.J., Chow, C.-Y., and Zhang, J.-D. (2017, January 21\u201323). SoCaST: Exploiting Social, Categorical and Spatio-Temporal Preferences for Personalized Event Recommendations. Proceedings of the 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC), Exeter, UK.","DOI":"10.1109\/ISPAN-FCST-ISCC.2017.68"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, X., Pan, A., Tang, L.-A., Li, Z., and Han, J. (2011, January 25\u201327). Geo-Friends Recommendation in GPS-based Cyber-physical Social Network. Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining, Kaohsiung, Taiwan.","DOI":"10.1109\/ASONAM.2011.118"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s10462-019-09684-w","article-title":"A study on features of social recommender systems","volume":"53","author":"Shokeen","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, B., Fu, Y., Yao, Z., and Xiong, H. (2013, January 11\u201314). Learning geographical preferences for point-of-interest recommendation. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2487673"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, J.-D., Chow, C.-Y., and Li, Y. (2014, January 4\u20137). LORE: Exploiting sequential influence for location recommendations. Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Dallas, TX, USA.","DOI":"10.1145\/2666310.2666400"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sun, Y., Yin, H., and Ren, X. (2017, January 3\u20137). Recommendation in context-rich environment: An information network analysis approach. Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia.","DOI":"10.1145\/3041021.3051105"},{"key":"ref_13","first-page":"824","article-title":"Context-Aware Point-of-Interest Recommendation in Location-Based Social Networks","volume":"40","author":"Ren","year":"2017","journal-title":"Jisuanji Xuebao Chin. J. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cui, Q., Tang, Y., Wu, S., and Wang, L. (2019). Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction. Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer.","DOI":"10.1007\/978-3-030-16142-2_23"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ye, M., Yin, P., Lee, W.-C., and Lee, D.-L. (2011, January 25\u201329). Exploiting geographical influence for collaborative point-of-interest recommendation. Proceedings of the 34th international ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, China.","DOI":"10.1145\/2009916.2009962"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, J.-D., and Chow, C.-Y. (2013, January 5\u20138). iGSLR: Personalized geo-social location recommendation: A kernel density estimation approach. Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Orlando, FL, USA.","DOI":"10.1145\/2525314.2525339"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., and Rui, Y. (2014, January 24\u201327). GeoMF: Joint geographical modeling and matrix factorization for point-of-interest recommendation. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2623330.2623638"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, J.-D., and Chow, C.-Y. (2015, January 9\u201313). GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile.","DOI":"10.1145\/2766462.2767711"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1109\/TSC.2014.2328341","article-title":"iGeoRec: A Personalized and Efficient Geographical Location Recommendation Framework","volume":"8","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guo, H., Li, X., He, M., Zhao, X., Liu, G., and Xu, G. (2016). CoSoLoRec: Joint Factor Model with Content, Social, Location for Heterogeneous Point-of-Interest Recommendation. International Conference on Knowledge Science, Engineering and Management, Springer.","DOI":"10.1007\/978-3-319-47650-6_48"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1109\/TSC.2015.2413783","article-title":"TICRec: A Probabilistic Framework to Utilize Temporal Influence Correlations for Time-Aware Location Recommendations","volume":"9","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.neucom.2017.08.020","article-title":"A personalized point-of-interest recommendation model via fusion of geo-social information","volume":"273","author":"Gao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_23","first-page":"17","article-title":"Fused matrix factorization with geographical and social influence in location-based social networks","volume":"1","author":"Cheng","year":"2012","journal-title":"Proc. Natl. Conf. Artif. Intell."},{"key":"ref_24","unstructured":"Yuan, Q., Cong, G., Ma, Z., Sun, A., and Thalmann, N.M. (August, January 28). Time-aware point-of-interest recommendation. Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yin, H., Sun, Y., Cui, B., Hu, Z., and Chen, L. (2013, January 11\u201314). LCARS: A location-content-aware recommender system. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2487608"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.ins.2014.09.014","article-title":"CoRe: Exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations","volume":"293","author":"Zhang","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_27","unstructured":"Liu, Y., Wei, W., Sun, A., and Miao, C. (2017, January 13\u201314). Exploiting Geographical Neighborhood Characteristics for Location Recommendation. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai, China."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, X., Cong, G., Li, X.-L., Pham, T.-A.N., and Krishnaswamy, S. (2015, January 9\u201313). Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile.","DOI":"10.1145\/2766462.2767722"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1080\/13658816.2018.1447671","article-title":"RecNet: A deep neural network for personalized POI recommendation in location-based social networks","volume":"32","author":"Ding","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_30","first-page":"5877","article-title":"Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation","volume":"33","author":"Zhao","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, Q., Wu, S., Wang, L., and Tan, T. (2016, January 12\u201317). Predicting the next location: A recurrent model with spatial and temporal contexts. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kong, D., and Wu, F. (2018, January 13\u201319). HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction. Proceedings of the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/324"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3182166","article-title":"GeoMF++: Scalable Location Recommendation via Joint Geographical Modeling and Matrix Factorization","volume":"36","author":"Lian","year":"2018","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_34","unstructured":"Guo, Q. (2019). Graph-based Point-of-interest Recommendation on Location-based Social Networks. [Ph.D. Thesis, Nanyang Technological University]."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Anagnostopoulos, A., Kumar, R., and Mahdian, M. (2008, January 24\u201327). Influence and correlation in social networks. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA.","DOI":"10.1145\/1401890.1401897"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Qiu, J., Tang, J., Ma, H., Dong, Y., Wang, K., and Tang, J. (2018, January 19\u201323). DeepInf: Modeling influence locality in large social networks. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK.","DOI":"10.1145\/3219819.3220077"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ma, H., King, I., and Lyu, M.R. (2009, January 19\u201323). Learning to recommend with social trust ensemble. Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Boston, MA, USA.","DOI":"10.1145\/1571941.1571978"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961209.1961212","article-title":"Improving Recommender Systems by Incorporating Social Contextual Information","volume":"29","author":"Ma","year":"2011","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lak, P. (2016, January 17\u201321). A Novel Approach to Define and Model Contextual Features in Recommender Systems. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy.","DOI":"10.1145\/2911451.2911481"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Konstas, I., Stathopoulos, V., and Jose, J.M. (2009, January 19\u201323). On social networks and collaborative recommendation. Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Boston, MA, USA.","DOI":"10.1145\/1571941.1571977"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chaney, A.J., Blei, D.M., and Eliassi-Rad, T. (2015, January 16\u201320). A probabilistic model for using social networks in personalized item recommendation. Proceedings of the 9th ACM Conference on Recommender Systems, Vienna, Austria.","DOI":"10.1145\/2792838.2800193"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, M., Zheng, X., Yang, Y., and Zhang, K. (2018, January 2\u20137). Collaborative filtering with social exposure: A modular approach to social recommendation. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11835"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/TPAMI.2016.2605085","article-title":"Social collaborative filtering by trust","volume":"39","author":"Yang","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Herlocker, J.L., Konstan, J.A., Borchers, A., and Riedl, J. (1999, January 15\u201319). An algorithmic framework for performing collaborative filtering. Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, Berkeley, CA, USA.","DOI":"10.1145\/312624.312682"},{"key":"ref_45","unstructured":"Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis, CRC Press."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ma, H., Yang, H., Lyu, M.R., and King, I. (2008, January 26\u201330). SoRec: Social recommendation using probabilistic matrix factorization. Proceedings of the 17th ACM Conference on Information and Knowledge Management, Napa Valley, CA, USA.","DOI":"10.1145\/1458082.1458205"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.eswa.2006.04.012","article-title":"An intelligent fuzzy-based recommendation system for consumer electronic products","volume":"33","author":"Cao","year":"2007","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s10708-016-9699-x","article-title":"Utilizing fuzzy set theory to assure the quality of volunteered geographic information","volume":"82","author":"Yan","year":"2017","journal-title":"GeoJournal"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1080\/15481603.2017.1413794","article-title":"Trust as a proxy indicator for intrinsic quality of Volunteered Geographic Information in biodiversity monitoring programs","volume":"55","author":"Vahidi","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1109\/TKDE.2017.2741484","article-title":"Spatial-aware hierarchical collaborative deep learning for POI recommendation","volume":"29","author":"Yin","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhang, F., Yuan, N.J., Lian, D., Xie, X., and Ma, W.-Y. (2016, January 13\u201317). Collaborative knowledge base embedding for recommender systems. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939673"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ma, H., Zhou, D., Liu, C., Lyu, M.R., and King, I. (2011, January 9\u201312). Recommender systems with social regularization. Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, Hong Kong, China.","DOI":"10.1145\/1935826.1935877"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1007\/s11280-018-0573-2","article-title":"Leveraging multi-aspect time-related influence in location recommendation","volume":"22","author":"Hosseini","year":"2019","journal-title":"World Wide Web"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/4\/285\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:33:25Z","timestamp":1760366005000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/4\/285"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,24]]},"references-count":53,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["ijgi9040285"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9040285","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2020,4,24]]}}}