{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T23:54:10Z","timestamp":1784246050298,"version":"3.55.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T00:00:00Z","timestamp":1623110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T00:00:00Z","timestamp":1623110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["cstc2020jcyj-msxmX0900"],"award-info":[{"award-number":["cstc2020jcyj-msxmX0900"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2020CDJ-LHZZ-040"],"award-info":[{"award-number":["2020CDJ-LHZZ-040"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mobile Netw Appl"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s11036-021-01782-w","type":"journal-article","created":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T04:02:47Z","timestamp":1623124967000},"page":"2434-2444","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["PR-RCUC: A POI Recommendation Model Using Region-Based Collaborative Filtering and User-Based Mobile Context"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3129-9052","authenticated-orcid":false,"given":"Jun","family":"Zeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhao","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,6,8]]},"reference":[{"key":"1782_CR1","unstructured":"Cheng C, Yang H, King I, Lyu MR (2012) Fused matrix factorization with geographical and social influence in location-based social networks. In: Aaai, vol 12, pp 17\u201323"},{"key":"1782_CR2","doi-asserted-by":"crossref","unstructured":"Fletcher K (2019) Regularizing matrix factorization with implicit user preference embeddings for web API recommendation. In: 2019 IEEE International conference on services computing (SCC), IEEE, pp 1\u20138","DOI":"10.1109\/SCC.2019.00014"},{"key":"1782_CR3","doi-asserted-by":"crossref","unstructured":"Gao H, Duan Y, Shao L, Sun X (2019) Transformation-based processing of typed resources for multimedia sources in the IoT environment. Wirel Netw, pp 1\u201317","DOI":"10.1007\/s11276-019-02200-6"},{"key":"1782_CR4","doi-asserted-by":"crossref","unstructured":"Gao H, Huang W, Duan Y (2020) The cloud-edge based dynamic reconfiguration to service workflow for mobile ecommerce environments: A QoS prediction perspective. ACM Transactions on Internet Technology","DOI":"10.1145\/3391198"},{"issue":"4","key":"1782_CR5","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1007\/s11036-020-01535-1","volume":"25","author":"H Gao","year":"2020","unstructured":"Gao H, Kuang L, Yin Y, Guo B, Dou K (2020) Mining consuming behaviors with temporal evolution for personalized recommendation in mobile marketing Apps. ACM\/Springer Mobile Networks and Applications (MONET) 25(4):1233\u2013 1248","journal-title":"ACM\/Springer Mobile Networks and Applications (MONET)"},{"key":"1782_CR6","doi-asserted-by":"crossref","unstructured":"Gao H, Liu C, Li Y, Yang X (2020) V2VR: Reliable hybrid-network-oriented V2V data transmission and routing considering RSUs and connectivity probability. IEEE Transactions on Intelligent Transportation Systems","DOI":"10.1109\/TITS.2020.2983835"},{"key":"1782_CR7","doi-asserted-by":"crossref","unstructured":"Gao H, Tang J, Hu X, Liu H, et al. (2015) Content-aware point of interest recommendation on location-based social networks. In: Aaai, vol 15, Citeseer, pp 1721\u20131727","DOI":"10.1609\/aaai.v29i1.9462"},{"issue":"5","key":"1782_CR8","doi-asserted-by":"publisher","first-page":"4532","DOI":"10.1109\/JIOT.2019.2956827","volume":"7","author":"H Gao","year":"2019","unstructured":"Gao H, Xu Y, Yin Y, Zhang W, Li R, Wang X (2019) Context-aware QoS prediction with neural collaborative filtering for internet-of-things services. IEEE Internet Things J 7(5):4532\u20134542","journal-title":"IEEE Internet Things J"},{"key":"1782_CR9","doi-asserted-by":"crossref","unstructured":"He J, Li X, Liao L (2017) Category-aware next point-of-interest recommendation via listwise bayesian personalized ranking. In: IJCAI, vol 17, pp 1837\u20131843","DOI":"10.24963\/ijcai.2017\/255"},{"key":"1782_CR10","doi-asserted-by":"crossref","unstructured":"He J, Li X, Liao L, Song D, Cheung WK (2016) Inferring a personalized next point-of-interest recommendation model with latent behavior patterns. In: Proceedings of the Thirtieth AAAI conference on artificial intelligence, pp 137\u2013143","DOI":"10.1609\/aaai.v30i1.9994"},{"key":"1782_CR11","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"1782_CR12","doi-asserted-by":"crossref","unstructured":"Hu S, Tu Z, Wang Z, Xu X (2019) A POI-sensitive knowledge graph based service recommendation method. In: 2019 IEEE International conference on services computing (SCC), IEEE, pp 197\u2013201","DOI":"10.1109\/SCC.2019.00041"},{"key":"1782_CR13","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"},{"key":"1782_CR14","doi-asserted-by":"crossref","unstructured":"Li H, Ge Y, Lian D, Liu H (2017) Learning user\u2019s intrinsic and extrinsic interests for point-of-interest recommendation: a unified approach. In: IJCAI, pp 2117\u20132123","DOI":"10.24963\/ijcai.2017\/294"},{"key":"1782_CR15","doi-asserted-by":"crossref","unstructured":"Li H, Hong R, Zhu S, Ge Y (2015) Point-of-interest recommender systems: a separate-space perspective. In: 2015 IEEE International conference on data mining, IEEE, pp 231\u2013240","DOI":"10.1109\/ICDM.2015.27"},{"key":"1782_CR16","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":"1782_CR17","doi-asserted-by":"crossref","unstructured":"Lian D, Wu Y, Ge Y, Xie X, Chen E (2020) Geography-aware sequential location recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2009\u20132019","DOI":"10.1145\/3394486.3403252"},{"key":"1782_CR18","doi-asserted-by":"crossref","unstructured":"Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: Thirtieth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"1782_CR19","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1016\/j.future.2018.07.008","volume":"89","author":"S Liu","year":"2018","unstructured":"Liu S, Wang L (2018) A self-adaptive point-of-interest recommendation algorithm based on a multi-order markov model. Futur Gener Comput Syst 89:506\u2013514","journal-title":"Futur Gener Comput Syst"},{"key":"1782_CR20","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: IJCAI, pp 1807\u20131813","DOI":"10.24963\/ijcai.2019\/250"},{"key":"1782_CR21","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":"1782_CR22","doi-asserted-by":"crossref","unstructured":"Manotumruksa J, Macdonald C, Ounis I (2017) A personalised ranking framework with multiple sampling criteria for venue recommendation. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1469\u20131478","DOI":"10.1145\/3132847.3132985"},{"key":"1782_CR23","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618"},{"key":"1782_CR24","doi-asserted-by":"crossref","unstructured":"Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285\u2013295","DOI":"10.1145\/371920.372071"},{"key":"1782_CR25","doi-asserted-by":"crossref","unstructured":"Tran T, Lee K, Liao Y, Lee D (2018) Regularizing matrix factorization with user and item embeddings for recommendation. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 687\u2013696","DOI":"10.1145\/3269206.3271730"},{"key":"1782_CR26","doi-asserted-by":"crossref","unstructured":"Xue HJ, Dai X, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: IJCAI, vol. 17, Melbourne, Australia, pp 3203\u20133209","DOI":"10.24963\/ijcai.2017\/447"},{"key":"1782_CR27","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":"1782_CR28","doi-asserted-by":"crossref","unstructured":"Ye M, Yin P, Lee WC, Lee DL (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp 325\u2013334","DOI":"10.1145\/2009916.2009962"},{"key":"1782_CR29","doi-asserted-by":"crossref","unstructured":"Zeng J, He X, Tang H, Wen J (2020) Predicting the next location: a self-attention and recurrent neural network model with temporal context. Transactions on Emerging Telecommunications Technologies, pp e3898","DOI":"10.1002\/ett.3898"},{"issue":"4","key":"1782_CR30","doi-asserted-by":"publisher","first-page":"40","DOI":"10.4018\/IJWSR.2019100103","volume":"16","author":"J Zeng","year":"2019","unstructured":"Zeng J, Li F, He X, Wen J (2019) Fused collaborative filtering with user preference, geographical and social influence for point of interest recommendation. Int J Web Serv Res (IJWSR) 16(4):40\u201352","journal-title":"Int J Web Serv Res (IJWSR)"},{"key":"1782_CR31","doi-asserted-by":"crossref","unstructured":"Zeng J, Tang H, Li Y, He X (2019) A deep learning model based on sparse matrix for point-of-interest recommendation. In: SEKE, pp 379\u2013492","DOI":"10.18293\/SEKE2019-156"},{"key":"1782_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":"1782_CR33","doi-asserted-by":"crossref","unstructured":"Zhao K, Zhang Y, Yin H, Wang J, Zheng K, Zhou X, Xing C (2020) Discovering subsequence patterns for next poi recommendation. In: Proceedings of the Twenty-Ninth international joint conference on artificial intelligence, pp 3216\u20133222","DOI":"10.24963\/ijcai.2020\/445"},{"key":"1782_CR34","unstructured":"Zhao S, Zhao T, King I, Lyu MR (2016) GT-SEER: Geo-temporal sequential embedding rank for point-of-interest recommendation. arXiv:1606.05859"}],"container-title":["Mobile Networks and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-021-01782-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11036-021-01782-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-021-01782-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T07:35:06Z","timestamp":1672385706000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11036-021-01782-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,8]]},"references-count":34,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["1782"],"URL":"https:\/\/doi.org\/10.1007\/s11036-021-01782-w","relation":{},"ISSN":["1383-469X","1572-8153"],"issn-type":[{"value":"1383-469X","type":"print"},{"value":"1572-8153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,8]]},"assertion":[{"value":"10 May 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}