{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:51:31Z","timestamp":1781110291563,"version":"3.54.1"},"reference-count":17,"publisher":"IGI Global Scientific Publishing","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>Prediction of app usage and location of smartphone users is an interesting problem and active area of research. Several smartphone sensors such as GPS, accelerometer, gyroscope, microphone, camera and Bluetooth make it easier to capture user behavior data and use it for appropriate analysis. However, differences in user behavior and increasing number of apps have made such prediction a challenging problem. In this article, a prediction approach that takes smartphone user behavior into consideration is proposed. The proposed approach is illustrated using data from over 30000 users from a leading IT company in China by first converting data in to recency, frequency, and monetary variables and then performing cluster analysis to capture user behavior. Prediction models are then developed for each cluster using a training dataset and their performance is assessed using a test dataset. The study involves ten different categories of apps and four different regions in Beijing. The proposed app usage prediction and next location prediction approach has provided interesting results.<\/jats:p>","DOI":"10.4018\/ijbir.2018070104","type":"journal-article","created":{"date-parts":[[2018,7,11]],"date-time":"2018-07-11T11:15:54Z","timestamp":1531307754000},"page":"64-80","source":"Crossref","is-referenced-by-count":1,"title":["Cluster-Based Smartphone Predictive Analytics for Application Usage and Next Location Prediction"],"prefix":"10.4018","volume":"9","author":[{"given":"Xiaoling","family":"Lu","sequence":"first","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bharatendra","family":"Rai","sequence":"additional","affiliation":[{"name":"University of Massachusetts Dartmouth, North Dartmouth, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Zhong","sequence":"additional","affiliation":[{"name":"Texas A&M University, College Station, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhu","family":"Li","sequence":"additional","affiliation":[{"name":"University of Massachusetts Dartmouth, North Dartmouth, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJBIR.2018070104-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2017.01.007"},{"key":"IJBIR.2018070104-1","doi-asserted-by":"publisher","DOI":"10.1108\/IMDS-04-2016-0141"},{"key":"IJBIR.2018070104-2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2013.03.006"},{"key":"IJBIR.2018070104-3","first-page":"4","article-title":"Boosting response with RFM.","author":"A.Hughes","year":"1996","journal-title":"American Demographics"},{"key":"IJBIR.2018070104-4","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2017.03.051"},{"key":"IJBIR.2018070104-5","article-title":"Antecedents of mobile app usage among smartphone users.","author":"S.Kim","year":"2014","journal-title":"Journal of Marketing Communications, 22(6), 653-670."},{"key":"IJBIR.2018070104-6","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/978-3-642-21726-5_6","article-title":"Learning time-based presence probabilities.","author":"J.Krumm","year":"2011","journal-title":"International Conference on Pervasive Computing"},{"key":"IJBIR.2018070104-7","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2013.07.014"},{"key":"IJBIR.2018070104-8","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2014.04.001"},{"key":"IJBIR.2018070104-9","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2016.04.026"},{"key":"IJBIR.2018070104-10","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-013-0175-x"},{"key":"IJBIR.2018070104-11","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2016.7474339"},{"key":"IJBIR.2018070104-12","doi-asserted-by":"publisher","DOI":"10.4018\/jthi.2005070101"},{"key":"IJBIR.2018070104-13","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1007\/978-3-642-21726-5_10","article-title":"Nextplace: a spatio-temporal prediction framework for pervasive systems.","author":"S.Scellato","year":"2011","journal-title":"International Conference on Pervasive Computing"},{"key":"IJBIR.2018070104-14","doi-asserted-by":"publisher","DOI":"10.1108\/IJRDM-11-2012-0108"},{"issue":"12","key":"IJBIR.2018070104-15","doi-asserted-by":"crossref","first-page":"2860","DOI":"10.1587\/transinf.E96.D.2860","article-title":"Apps at hand: Personalized live homescreen based on mobile app usage prediction.","volume":"96","author":"X.Xia","year":"2013","journal-title":"IEICE Transactions on Information and Systems"},{"key":"IJBIR.2018070104-16","author":"P.Zikopoulos","year":"2011","journal-title":"Understanding big data: Analytics for enterprise class Hadoop and streaming data"}],"container-title":["International Journal of Business Intelligence Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=209704","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T06:29:53Z","timestamp":1651818593000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJBIR.2018070104"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":17,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.4018\/ijbir.2018070104","relation":{},"ISSN":["1947-3591","1947-3605"],"issn-type":[{"value":"1947-3591","type":"print"},{"value":"1947-3605","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7]]}}}