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24 May 2022 Data driven user feature prediction in mobile applications based on multi-channel CNN
Yuanbang Li, Chi Xu, Shi Dong, Laihang Yu
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Proceedings Volume 12260, International Conference on Computer Application and Information Security (ICCAIS 2021); 122601M (2022) https://doi.org/10.1117/12.2637606
Event: International Conference on Computer Application and Information Security (ICCAIS 2021), 2021, Wuhan, China
Abstract
With the continuous development of mobile positioning technology and smart phones, users can use smart phones to obtain and share location information of themselves and various surrounding points of interest (POI) anytime, anywhere, and share their own activity information, thus forming a location-based social network (LBSN). A large amount of user data is generated in LBSN. How to quantitative analyze the effects of context to manager’s view, how to extract the hidden user feature through the analysis of user data is of important research significance for the intelligent analysis of user characteristics. In this paper, first, a muti-dimensional user feature construction method is proposed, which extracts user feature from different influencing factors. Second, the fitness of user to a feature is analyzed. Third, a unified model is used to characterize this applicability. The method can promote the transformation from user data to user feature and help solve the problem of “explosive data but poor knowledge”. Experimental verification shows that the method is feasible and can realize the mining of muti-dimensional feature of users.
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Yuanbang Li, Chi Xu, Shi Dong, and Laihang Yu "Data driven user feature prediction in mobile applications based on multi-channel CNN", Proc. SPIE 12260, International Conference on Computer Application and Information Security (ICCAIS 2021), 122601M (24 May 2022); https://doi.org/10.1117/12.2637606
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KEYWORDS
Data modeling
Analytical research
Cell phones
Feature extraction
Lithium
Machine learning
Mining
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