Abstract
In the era of big data (BD), consumer behavior information is easier to collect. Therefore, as a popular e-commerce (EC) industry, it is necessary to use data mining (DM) technology to fully understand the needs of customers and make related product recommendations. Therefore, from the background of BD, this article uses DM methods to study user preferences that can increase the sales and benefits of EC companies. The main methods used in this paper are data collection method and experimental method. The application of DM in user preferences and the personalized recommendation system of EC enterprises are studied. The experimental results show that the design accuracy and coverage of this system can reach data above 0.9 under a certain threshold, but there is still an unstable phenomenon, and it is necessary to choose a good DM method for further improvement.
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Yang, L., Yin, X. (2022). Data Mining of E-Commerce Enterprise User Preferences in the Context of Big Data. In: Xu, Z., Alrabaee, S., Loyola-González, O., Zhang, X., Cahyani, N.D.W., Ab Rahman, N.H. (eds) Cyber Security Intelligence and Analytics. CSIA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 123. Springer, Cham. https://doi.org/10.1007/978-3-030-96908-0_87
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DOI: https://doi.org/10.1007/978-3-030-96908-0_87
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