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Demographic information prediction: a portrait of smartphone application Users
journal contribution
posted on 2023-06-09, 01:13 authored by Zhen Qin, Yilei Wang, Hongrong Cheng, Yingjie Zhou, Zhengguo ShengZhengguo Sheng, Victor C M LeungDemographic information is usually treated as private data (e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. Then, by considering the category-unbalance problem, we make use of the correlation between users’ demographic information and their requested Internet resources to make the prediction, and propose an optimal method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21% of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance (e.g., 73.84% of Accuracy in the prediction of age group and 66.42% of Accuracy in the prediction of phone level).
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Publication status
- Published
File Version
- Accepted version
Journal
IEEE Transactions on Emerging Topics in ComputingISSN
2168-6750Publisher
Institute of Electrical and Electronics EngineersExternal DOI
Issue
99Department affiliated with
- Engineering and Design Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2016-05-13First Open Access (FOA) Date
2016-11-08First Compliant Deposit (FCD) Date
2016-05-13Usage metrics
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