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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03500naa a2200409 4500 | |
| 001 | oai:DiVA.org:kth-298281 | |
| 003 | SwePub | |
| 008 | 210929s2021 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-2982812 URI |
| 024 | 7 | a https://doi.org/10.3390/info120501802 DOI |
| 040 | a (SwePub)kth | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Jiang, Haiyangu Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing 100876, Peoples R China.4 aut |
| 245 | 1 0 | a Machine-Learning-Based User Position Prediction and Behavior Analysis for Location Services |
| 264 | c 2021-04-22 | |
| 264 | 1 | b MDPI AG,c 2021 |
| 338 | a print2 rdacarrier | |
| 500 | a QC 20210929 | |
| 520 | a 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' 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' 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' 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-accuracy 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. | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Systemvetenskap, informationssystem och informatik0 (SwePub)102022 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Information Systems0 (SwePub)102022 hsv//eng |
| 653 | a location-based service | |
| 653 | a machine learning | |
| 653 | a XGBoost | |
| 653 | a behavior analysis | |
| 700 | 1 | a He, Mingshuu Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China.4 aut |
| 700 | 1 | a Xi, Yuanyuanu KTH,Skolan för elektroteknik och datavetenskap (EECS)4 aut0 (Swepub:kth)u1sl884b |
| 700 | 1 | a Zeng, Jianqiuu Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing 100876, Peoples R China.4 aut |
| 710 | 2 | a Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing 100876, Peoples R China.b Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China.4 org |
| 773 | 0 | t Informationd : MDPI AGg 12:5q 12:5x 2078-2489 |
| 856 | 4 | u https://doi.org/10.3390/info12050180y Fulltext |
| 856 | 4 | u https://www.mdpi.com/2078-2489/12/5/180/pdf |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298281 |
| 856 | 4 8 | u https://doi.org/10.3390/info12050180 |
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