{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:30:18Z","timestamp":1750221018070,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":15,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T00:00:00Z","timestamp":1563494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["71601052"],"award-info":[{"award-number":["71601052"]}]},{"name":"the Hundred Young Talent Project of Guangdong University of Technology","award":["220413637"],"award-info":[{"award-number":["220413637"]}]},{"name":"the funding of High-Level University Development for Guangdong University of Technology","award":["262511006"],"award-info":[{"award-number":["262511006"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,19]]},"DOI":"10.1145\/3352411.3352449","type":"proceedings-article","created":{"date-parts":[[2019,8,29]],"date-time":"2019-08-29T12:18:43Z","timestamp":1567081123000},"page":"242-248","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Modeling Customers' Loyalty Using Ten Years' Automobile Repair and Maintenance Data"],"prefix":"10.1145","author":[{"given":"Sheng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueliang","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianghang","family":"Chen","sequence":"additional","affiliation":[{"name":"Antai College of Economics and Management, Shanghai Jiaotong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinjun","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Electro-Mechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,7,19]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2016.16"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2010.12.014"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/01449290512331321938"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2006.01.007"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.07.053"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.01.031"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2016.11.007"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jretconser.2017.07.007"},{"key":"e_1_3_2_1_9_1","volume-title":"et al","author":"Konopka B. M.","year":"2018","unstructured":"Konopka , B. M. et al . 2018 . \"Exploratory data analysis of a clinical study group: Development of a procedure for exploring multidimensional data.\". Plos One 13(8). Konopka, B. M. et al. 2018. \"Exploratory data analysis of a clinical study group: Development of a procedure for exploring multidimensional data.\". Plos One 13(8)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"X. Lai H. Fu J. Li Z. Sha 2018. Understanding Drivers' Route Choice Behaviours in the Urban Network with Machine Learning Models. IET Intelligent Transport Systems forthcoming.  X. Lai H. Fu J. Li Z. Sha 2018. Understanding Drivers' Route Choice Behaviours in the Urban Network with Machine Learning Models. IET Intelligent Transport Systems forthcoming.","DOI":"10.1049\/iet-its.2018.5190"},{"key":"e_1_3_2_1_11_1","unstructured":"Hornik K. 2017-10-04. R FAQ. The Comprehensive R Archive Network. Retrieved 2018-08-06.  Hornik K. 2017-10-04. R FAQ. The Comprehensive R Archive Network. Retrieved 2018-08-06."},{"key":"e_1_3_2_1_12_1","unstructured":"Vance A. Data Analysts Captivated by R's Power. New York Times 2009-01-06. Retrieved 2018-08-06  Vance A. Data Analysts Captivated by R's Power. New York Times 2009-01-06. Retrieved 2018-08-06"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2016.01.011"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2011.11.002"},{"key":"e_1_3_2_1_15_1","volume-title":"New York","author":"Hastie T.","year":"2009","unstructured":"Hastie , T. Tibshirani , R. and Friedman , J . 2009 . The Elements of Statistical Learning : data mining, inference, and prediction, 2 nd ed., New York : Springer , 587--604 Hastie, T. Tibshirani, R. and Friedman, J. 2009. The Elements of Statistical Learning: data mining, inference, and prediction, 2nd ed., New York: Springer, 587--604","edition":"2"}],"event":{"name":"DSIT 2019: 2019 2nd International Conference on Data Science and Information Technology","sponsor":["The Hong Kong Polytechnic The Hong Kong Polytechnic University","Natl University of Singapore National University of Singapore"],"location":"Seoul Republic of Korea","acronym":"DSIT 2019"},"container-title":["Proceedings of the 2019 2nd International Conference on Data Science and Information Technology"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3352411.3352449","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3352411.3352449","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:16Z","timestamp":1750206376000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3352411.3352449"}},"subtitle":["Machine Learning Approaches"],"short-title":[],"issued":{"date-parts":[[2019,7,19]]},"references-count":15,"alternative-id":["10.1145\/3352411.3352449","10.1145\/3352411"],"URL":"https:\/\/doi.org\/10.1145\/3352411.3352449","relation":{},"subject":[],"published":{"date-parts":[[2019,7,19]]},"assertion":[{"value":"2019-07-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}