Application of Internet User Behavior Data in Library Big Data Analysis

Fan Yang

doi:10.62177/apemr.v1i1.260

Authors

Fan Yang National Library of China

DOI:

https://doi.org/10.62177/apemr.v1i1.260

Keywords:

Big Data Management, Intelligent Application, Library, Management Systems, User Behavior Data, Data Analysis

Abstract

This paper investigates the current state of data utilization in libraries, thoroughly analyzing the characteristics, collection methods, and processing techniques of internet user behavior data. In response to the service demands of libraries, it proposes application models such as user profiling, personalized recommendation services, and optimized collection decision-making. Furthermore, the paper offers suggestions for building a data governance system and an open knowledge service platform, aiming to enhance libraries’ data-driven service capabilities. These strategies align with the central government's directive to “promote innovation in big data technology and industry development, build a digital economy driven by data, use big data to modernize national governance, improve public welfare, and safeguard national data security” ^[1]

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References

Xi J P. Seizing opportunities and proactively implementing the national big data strategy to accelerate the construction of Digital China. CPC News, 2017-12-09. http://cpc.people.com.cn/

Gallagher J, Bauer K, & Dollar D M. Evidence-based librarianship: Utilizing data from all available sources to make judicious print cancellation decisions. Library Collections, Acquisitions and Technical Services, 2005, 29(2): 169–179. DOI: https://doi.org/10.1080/14649055.2005.10766049

Cheng L J. The practice and implications of big data applications in the U.S.—A library perspective. Information and Documentation Services, 2013, 34(5): 110–112.

Chen H L, Doty P, Yu J C, et al. Library assessment and data analytics in the big data era: Practice and policies. Proceedings of the Association for Information Science and Technology, 2015, 52(1): 1–4. DOI: https://doi.org/10.1002/pra2.2015.14505201002

Fan W H, Li C H, Zhang X W, et al. What kind of "big data" do libraries need? Library Journal, 2012, 31(11): 63–68.

Han C F. The impact and challenges of big data for libraries. Library and Information, 2012, (5): 37–40.

Liu W, Xia C J, & Zhang C J. Big data and linked data: An upcoming data technology revolution. New Technology of Library and Information Service, 2013, (4): 2–9.

Li Y K. Statistical methods for user profiling in the context of big data. Master's thesis. Beijing: Capital University of Economics and Business, 2016.

Liu Y. The application status and prospects of folksonomy. Modern Economic Information, 2010, (5): 205–206.

Chen L B. Folksonomy and its application in libraries. Journal of the Party School of Sichuan Provincial Committee of the CPC, 2010, (2): 93–96.

Wang S, & Xu X. Establishing structured folksonomy based on user classification tags. Library Science Research, 2011, (9): 73–76.

Yang F. Library big data practice based on user profiling: A case study of the National Library of China. Library Forum, 2019, 39(2): 58–64.

Technology Innovation and Social Responsibility Bureau. Notice on accelerating the digital transformation of state-owned enterprises. (2020-07-21) [2023-03-15]. http://www.sasac.gov.cn/

The State Council of China. Notice on the '14th Five-Year Plan' for Digital Economy Development. (2021-12-12) [2023-03-15]. https://www.gov.cn/