User profiles and precision analysis based on Gen Z short video behavior with FP-Growth algorithm

Huijun Ni; Boxi Zhou; Xinlei Zhang Ni

doi:10.1117/12.3008244

14 November 2023 User profiles and precision analysis based on Gen Z short video behavior with FP-Growth algorithm

Huijun Ni, Boxi Zhou, Xinlei Zhang Ni

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 1293408 (2023) https://doi.org/10.1117/12.3008244
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

Analyzing the relationship between GEN Z employees job performance and online Short Video behavior is the focus of employment management and guidance in companies under the background of big data. This paper extracts the characteristics of employees' job burnout information and online Short Video behavior data, constructs the student label model with new employees as the research object, we use association rules to mine the relationship between employers performance and online behavior, and analyze the behavior characteristics with different performance. The results show that three typical portraits exists in A employees, three typical paths in A employees, two typical paths in C employees. These results guide employees’ online short video behavior, and provide the application of association rule mining and behavior analysis in job performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huijun Ni, Boxi Zhou, and Xinlei Zhang Ni "User profiles and precision analysis based on Gen Z short video behavior with FP-Growth algorithm", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 1293408 (14 November 2023); https://doi.org/10.1117/12.3008244

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