research-article

Construct the Students' Consumption Portrait Based on Large-scale Campus Data

Authors:

Heng Li,

Xiao Zhang,

Xia Li,

Yanqiu Wang,

Yulong Ling,

Di MuAuthors Info & Claims

ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and Computing

Pages 198 - 201

https://doi.org/10.1145/3383972.3384019

Published: 26 May 2020 Publication History

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Abstract

The consumption data of campus card can effectively represent students' life records, and it is extremely important to deeply understand their consumption feature. In this paper, we proposed a method to describe students' consumption behavior by using features. According to the students' consumption records of campus card, we extracted 7 features from three aspects of their living habits, affluence and life trajectory. Three different levels of values were assigned for each feature to illustrate the consumption differences among students. Through these features, we constructed portraits of campus consumption for students. We could attach labels to each student to describe their campus consumption behavior and show their different living habits from others.

References

[1]

Huifeng Guo. Personalized recommendation model and algorithm based on user behavior relationship mining [D]. 2018.

Google Scholar

[2]

Ziyan Jin, Juan Zhang, Xiangjun Li, et al. A collaborative filtering advertising recommendation algorithm with tags [J]. Computer engineering, 2018, 44(4).

Google Scholar

[3]

Xiaofu Yang, Xinshu Qin. Establishment of doctor recommendation model based on electronic medical record using matrix multiplication [J]. Computer and modernization (06): 81.

Google Scholar

[4]

Qian Ma. Analysis of college students' eating behavior based on consumption data of campus card [D][D]. Changchun: northeast normal university, 2015.

Google Scholar

[5]

Jinhua Ren. Study on the rules of students' campus consumption activities based on the data of one-card [D]. Central China normal university, 2018.

Google Scholar

[6]

Rui Tong. Research on student performance prediction and friend relationship network detection based on the consumption data of one-card [D]. Master's thesis. Wuhan: central China normal university, 2016.

Google Scholar

[7]

Nan Jiang, Weisheng Xu. Analysis of students' consumption and learning behavior based on campus one-card data [J]. Microcomputer application, 2015, 31(2): 35--38.

Google Scholar

[8]

Zhihong Liu, Xiaoxu Yu. Analysis of consumption behavior of campus one-card based on data mining [J]. Information recording materials, 2018, 19(12): 95--96.

Google Scholar

[9]

Guo Cheng. Analysis and application of campus card consumption data based on data mining [D]. Wuhan light industry university, 2017.

Google Scholar

[10]

Yuting Duan. Data mining and application research on student consumption information based on campus CARDS [D]. Southwest university of science and technology, 2018.

Google Scholar

[11]

Honglei Zhang. Research and application of data mining in campus card consumption [D]. Lanzhou jiaotong university, 2016.

Google Scholar

[12]

Hongwei Ma. Mining and analysis of consumption data of campus one-card [D]. Shanxi normal university, 2016.

Google Scholar

Cited By

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Wang YWen JZhou WTao BWu QFu CLi H(2023)An early warning method for abnormal behavior of college students based on multimodal fusion and improved decision treeJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23150945:5(8405-8427)Online publication date: 4-Nov-2023
https://doi.org/10.3233/JIFS-231509
Zhu JLi BWang Z(2023)A poverty index prediction model for students based on PSO-LightGBMAnnals of Operations Research10.1007/s10479-023-05652-4Online publication date: 3-Nov-2023
https://doi.org/10.1007/s10479-023-05652-4
Chen WYao JLiu YLi JSun YPang C(2022)Psychological Attention-based Analytics of Multivariate Campus Behaviors of University Students2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10021007(4656-4663)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10021007
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Index Terms

Construct the Students' Consumption Portrait Based on Large-scale Campus Data
1. Human-centered computing
  1. Visualization
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and Computing

February 2020

607 pages

ISBN:9781450376426

DOI:10.1145/3383972

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shenzhen University: Shenzhen University

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Association for Computing Machinery

New York, NY, United States

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Published: 26 May 2020

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National Key Research and Development Program
the NFS of China under Grant
NFS of China under Grant

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ICMLC 2020

ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing

February 15 - 17, 2020

Shenzhen, China

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Wang YWen JZhou WTao BWu QFu CLi H(2023)An early warning method for abnormal behavior of college students based on multimodal fusion and improved decision treeJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23150945:5(8405-8427)Online publication date: 4-Nov-2023
https://doi.org/10.3233/JIFS-231509
Zhu JLi BWang Z(2023)A poverty index prediction model for students based on PSO-LightGBMAnnals of Operations Research10.1007/s10479-023-05652-4Online publication date: 3-Nov-2023
https://doi.org/10.1007/s10479-023-05652-4
Chen WYao JLiu YLi JSun YPang C(2022)Psychological Attention-based Analytics of Multivariate Campus Behaviors of University Students2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10021007(4656-4663)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10021007
Feng GFan MAo C(2022)Exploration and Visualization of Learning Behavior Patterns From the Perspective of Educational Process MiningIEEE Access10.1109/ACCESS.2022.318411110(65271-65283)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3184111
Ling YZhang X(2021)An Improved K-means Algorithm Based on Multiple Clustering and DensityProceedings of the 2021 13th International Conference on Machine Learning and Computing10.1145/3457682.3457695(86-92)Online publication date: 26-Feb-2021
https://dl.acm.org/doi/10.1145/3457682.3457695

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Profiling Energy Consumption in a Residential Campus

On-campus and off-campus first-year electrical engineering students engage in interactive learning environments

Study on real energy consumption of large-scale campus wireless network

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