{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T19:36:02Z","timestamp":1761766562286,"version":"3.37.3"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772282","61772454","61811530332","61811540410"],"award-info":[{"award-number":["61772282","61772454","61811530332","61811540410"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Hum. Cent. Comput. Inf. Sci."],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Location-based mobile marketing recommendation has become one of the hot spots in e-commerce. The current mobile marketing recommendation system only treats location information as a recommended attribute, which weakens the role of users and shopping location information in the recommendation. This paper focuses on location feedback data of user and proposes a location-based mobile marketing recommendation model by convolutional neural network (LBCNN). First, the users\u2019 location-based behaviors are divided into different time windows. For each window, the extractor achieves users\u2019 timing preference characteristics from different dimensions. Next, we use the convolutional model in the convolutional neural network model to train a classifier. The experimental results show that the model proposed in this paper is better than the traditional recommendation models in the terms of accuracy rate and recall rate, both of which increase nearly 10%.<\/jats:p>","DOI":"10.1186\/s13673-019-0177-6","type":"journal-article","created":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:03:08Z","timestamp":1556668988000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Mobile marketing recommendation method based on user location feedback"],"prefix":"10.1186","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5764-2432","authenticated-orcid":false,"given":"Chunyong","family":"Yin","sequence":"first","affiliation":[]},{"given":"Shilei","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Jin","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,1]]},"reference":[{"issue":"2\u20133","key":"177_CR1","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s11257-016-9172-z","volume":"26","author":"I Fern\u00e1ndez-Tob\u00edas","year":"2016","unstructured":"Fern\u00e1ndez-Tob\u00edas I, Braunhofer M, Elahi M, Ricci F, Cantador I (2016) Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model User Adap Inter 26(2\u20133):221\u2013255","journal-title":"User Model User Adap Inter"},{"key":"177_CR2","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.measurement.2016.05.058","volume":"91","author":"H Koohi","year":"2016","unstructured":"Koohi H, Kiani K (2016) User based collaborative filtering using fuzzy C-means. Measurement 91:134\u2013139","journal-title":"Measurement"},{"key":"177_CR3","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.jnca.2018.09.006","volume":"124","author":"X Xu","year":"2018","unstructured":"Xu X, Fu S, Qi L, Zhang X, Liu Q, He Q, Li S (2018) An IoT-oriented data placement method with privacy preservation in cloud environment. J Netw Comput Appl 124:148\u2013157","journal-title":"J Netw Comput Appl"},{"issue":"12","key":"177_CR4","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/CC.2015.7385528","volume":"12","author":"X Yingyuan","year":"2015","unstructured":"Yingyuan X, Pengqiang A, Ching-Hsien H, Hongya W, Xu J (2015) Time-ordered collaborative filtering for news recommendation. China Commun 12(12):53\u201362","journal-title":"China Commun"},{"key":"177_CR5","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/978-3-642-22362-4_16","volume-title":"User Modeling, Adaption and Personalization","author":"Marius Kaminskas","year":"2011","unstructured":"Kaminskas M, Ricci F (2011) Location-adapted music recommendation using tags. In: International conference on user modeling, adaptation, and personalization. pp 183\u2013194"},{"issue":"4","key":"177_CR6","first-page":"58","volume":"5","author":"H Zhu","year":"2015","unstructured":"Zhu H, Chen E, Xiong H, Yu K, Cao H, Tian J (2015) Mining mobile user preferences for personalized context-aware recommendation. ACM Trans Intell Syst Technol TIST 5(4):58","journal-title":"ACM Trans Intell Syst Technol TIST"},{"issue":"3","key":"177_CR7","first-page":"19","volume":"9","author":"H Yin","year":"2015","unstructured":"Yin H, Cui B, Chen L, Hu Z, Zhang C (2015) Modeling location-based user rating profiles for personalized recommendation. ACM Trans Knowl Discov Data TKDD 9(3):19","journal-title":"ACM Trans Knowl Discov Data TKDD"},{"key":"177_CR8","doi-asserted-by":"crossref","unstructured":"Li X, Xu G, Chen E, Li L (2015) Learning user preferences across multiple aspects for merchant recommendation. In: 2015 IEEE international conference on data mining (ICDM). pp 865\u2013870","DOI":"10.1109\/ICDM.2015.10"},{"issue":"8","key":"177_CR9","doi-asserted-by":"publisher","first-page":"3628","DOI":"10.1109\/TII.2017.2773646","volume":"14","author":"C Yin","year":"2018","unstructured":"Yin C, Xi J, Sun R, Wang J (2018) Location privacy protection based on differential privacy strategy for big data in industrial internet-of-things. IEEE Trans Industr Inf 14(8):3628\u20133636","journal-title":"IEEE Trans Industr Inf"},{"key":"177_CR10","doi-asserted-by":"crossref","unstructured":"Lian D, Ge Y, Zhang F, Yuan NJ, Xie X, Zhou T, Rui Y (2015) Content-aware collaborative filtering for location recommendation based on human mobility data. In: 2015 IEEE international conference on data mining. pp 261\u2013270","DOI":"10.1109\/ICDM.2015.69"},{"issue":"24","key":"177_CR11","doi-asserted-by":"publisher","first-page":"16719","DOI":"10.1007\/s11042-015-2915-8","volume":"75","author":"WP Lee","year":"2016","unstructured":"Lee WP, Tseng GY (2016) Incorporating contextual information and collaborative filtering methods for multimedia recommendation in a mobile environment. Multimedia Tools Appl 75(24):16719\u201316739","journal-title":"Multimedia Tools Appl"},{"issue":"1","key":"177_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1\u2013127","journal-title":"Found Trends Mach Learn"},{"issue":"7553","key":"177_CR13","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"4","key":"177_CR14","first-page":"357","volume":"53","author":"C Yuan","year":"2017","unstructured":"Yuan C, Li X, Wu QJ, Li J, Sun X (2017) Fingerprint liveness detection from different fingerprint materials using convolutional neural network and principal component analysis. Comput Mater Continua 53(4):357\u2013372","journal-title":"Comput Mater Continua"},{"key":"177_CR15","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.neucom.2016.07.079","volume":"256","author":"C Yin","year":"2017","unstructured":"Yin C, Wang J, Park JH (2017) An improved recommendation algorithm for big data cloud service based on the trust in sociology. Neurocomputing 256:49\u201355","journal-title":"Neurocomputing"},{"key":"177_CR16","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.patrec.2014.01.008","volume":"42","author":"M L\u00e4ngkvist","year":"2014","unstructured":"L\u00e4ngkvist M, Karlsson L, Loutfi A (2014) A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn Lett 42:11\u201324","journal-title":"Pattern Recogn Lett"},{"issue":"2","key":"177_CR17","first-page":"243","volume":"55","author":"Y Tu","year":"2018","unstructured":"Tu Y, Lin Y, Wang J, Kim JU (2018) Semi-supervised learning with generative adversarial networks on digital signal modulation classification. Comput Mater Continua 55(2):243\u2013254","journal-title":"Comput Mater Continua"},{"issue":"8","key":"177_CR18","doi-asserted-by":"publisher","first-page":"3879","DOI":"10.1016\/j.eswa.2013.12.023","volume":"41","author":"M Nilashi","year":"2014","unstructured":"Nilashi M, Bin Ibrahim O, Ithnin N (2014) Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Syst Appl 41(8):3879\u20133900","journal-title":"Expert Syst Appl"},{"issue":"1","key":"177_CR19","first-page":"121","volume":"55","author":"D Zeng","year":"2018","unstructured":"Zeng D, Dai Y, Li F, Sherratt RS, Wang J (2018) Adversarial learning for distant supervised relation extraction. Comput Mater Continua 55(1):121\u2013136","journal-title":"Comput Mater Continua"},{"issue":"13","key":"177_CR20","doi-asserted-by":"publisher","first-page":"4185","DOI":"10.1007\/s00500-017-2708-2","volume":"22","author":"C Yin","year":"2018","unstructured":"Yin C, Xia L, Zhang S, Sun R, Wang J (2018) Improved clustering algorithm based on high-speed network data stream. Soft Comput 22(13):4185\u20134195","journal-title":"Soft Comput"},{"key":"177_CR21","first-page":"1097","volume-title":"Advances in neural information processing systems","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"issue":"4","key":"177_CR22","first-page":"863","volume":"13","author":"X Li","year":"2017","unstructured":"Li X, Yao C, Fan F, Yu X (2017) A text similarity measurement method based on singular value decomposition and semantic relevance. J Inf Process Syst 13(4):863\u2013875","journal-title":"J Inf Process Syst"},{"key":"177_CR23","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neunet.2015.01.003","volume":"65","author":"CN Li","year":"2015","unstructured":"Li CN, Shao YH, Deng NY (2015) Robust L1-norm two-dimensional linear discriminant analysis. Neural Networks 65:92\u2013104","journal-title":"Neural Networks"},{"issue":"5","key":"177_CR24","first-page":"1397","volume":"13","author":"MA Fattah","year":"2017","unstructured":"Fattah MA (2017) A novel statistical feature selection approach for text categorization. J Inf Process Syst 13(5):1397\u20131409","journal-title":"J Inf Process Syst"},{"key":"177_CR25","doi-asserted-by":"crossref","unstructured":"Wang Y, Feng D, Li D, Chen X, Zhao Y, Niu X (2016) A mobile recommendation system based on logistic regression and Gradient Boosting Decision Trees. In: International joint conference on neural networks. pp 1896\u20131902","DOI":"10.1109\/IJCNN.2016.7727431"},{"issue":"7","key":"177_CR26","doi-asserted-by":"publisher","first-page":"e3902","DOI":"10.1002\/cpe.3902","volume":"29","author":"C Yin","year":"2017","unstructured":"Yin C, Zhang S, Xi J, Wang J (2017) An improved anonymity model for big data security based on clustering algorithm. Concurr Comput Pract Exp 29(7):e3902","journal-title":"Concurr Comput Pract Exp"}],"container-title":["Human-centric Computing and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13673-019-0177-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13673-019-0177-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13673-019-0177-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T11:36:20Z","timestamp":1627644980000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13673-019-0177-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,1]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["177"],"URL":"https:\/\/doi.org\/10.1186\/s13673-019-0177-6","relation":{},"ISSN":["2192-1962"],"issn-type":[{"type":"electronic","value":"2192-1962"}],"subject":[],"published":{"date-parts":[[2019,5,1]]},"assertion":[{"value":"25 September 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 May 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"14"}}