{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T17:36:08Z","timestamp":1783445768689,"version":"3.54.6"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T00:00:00Z","timestamp":1619049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Machine learning (ML)-based methods are increasingly used in different fields of business to improve the quality and efficiency of services. The increasing amount of data and the development of artificial intelligence algorithms have improved the services provided to customers in shopping malls. Most new services are based on customers\u2019 precise positioning in shopping malls, especially customer positioning within shops. We propose a novel method to accurately predict the specific shops in which customers are located in shopping malls. We use global positioning system (GPS) information provided by customers\u2019 mobile terminals and WiFi information that completely covers the shopping mall. According to the prediction results, we learn some of the behavior preferences of users. We use these predicted customer locations to provide customers with more accurate services. Our training dataset is built using feature extraction and screening from some real customers\u2019 transaction records in shopping malls. In order to prove the validity of the model, we also cross-check our algorithm with a variety of machine learning algorithms. Our method achieves the best speed\u2013accuracy trade-off and can accurately locate the shops in which customers are located in shopping malls in real time. Compared to other algorithms, the proposed model is more accurate. User preference behaviors can be used in applications to efficiently provide more tailored services.<\/jats:p>","DOI":"10.3390\/info12050180","type":"journal-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T04:20:17Z","timestamp":1619065217000},"page":"180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Machine-Learning-Based User Position Prediction and Behavior Analysis for Location Services"],"prefix":"10.3390","volume":"12","author":[{"given":"Haiyang","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2896-4595","authenticated-orcid":false,"given":"Mingshu","family":"He","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Xi","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqiu","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102762","DOI":"10.1016\/j.jnca.2020.102762","article-title":"ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services","volume":"168","author":"Sarker","year":"2020","journal-title":"J. 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