Abstract
In today’s society, the Internet is rapidly developing, Internet merchants can use the user information they have mastered to analyze consumer preferences. Then they conduct product recommendations to maximize profits. The accumulation of data on the behavior of consumers browsing, purchasing, and viewing advertisements on the Internet has become the basic source of information used by Internet companies to analyze users. The Internet merchant platform uses the behavioral preference data of the users that have been mastered before to analyze the different usage habits of users of different consumption levels and their usage requirements. However, combined with the characteristics of e-commerce, it can be found that this kind of killing phenomenon is not only reflected in the price differential pricing, but the quality difference and service difference on the same price basis may become the target of the merchant platform. The main contributions of this dissertation are as follows:(1) Research on big data killing against technology. (2) Design and implementation of big data killing system. The fuzzy Internet platform builds the consumer user portrait to achieve the goal of combating big data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, J.: Big data: a survey. Mob. Netw. Appl. 19(2), 171–209 (2014)
Gossett, E.: Big data: a revolution that will transform how we live, work, and think. Math. Comput. Educ. 47(17), 181–183 (2014)
Gu, H.: Modeling of user portrait through social media. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 13–15 (2018)
Gu, H.: Modeling of user portrait through social media. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 17–19 (2018)
Moody, K.: SharpSpider: spidering the Web through web services. In: American Web Congress, pp. 154–160 (2003)
Mukhopadhyay, D.: A new approach to design domain specific ontology based web crawler. In: International Conference on Information Technology (2007)
Giorgini, P.: Goal-oriented requirement analysis for data warehouse design. In: ACM International Workshop on Data Warehousing & OLAP, pp. 189–231 (2005)
Aldanondo, M.: Mass customization and configuration: requirement analysis and constraint based modeling propositions. Integr. Comput. Aided Eng. 10(2), 177–189 (2003)
Lesleyh, R.: Python phylogenetics: inference from morphology and mitochondrial DNA. Biol. J. Linn. Soc. 93(3), 603–619 (2010)
Al-Rfou, R.: Theano: a Python framework for fast computation of mathematical expressions, pp. 129–201 (2016)
White, G.C.: Program MARK: survival estimation from populations of marked animals. Bird Stud. 46(sup1), 125–140 (2013)
Feng, J.: Big data and privacy protection. Chin. J. Comput. 37, 246–258 (2014)
Zeng, J.: Big data user portrait and precision marketing based on Weibo. Mod. Econ. Inf. (16), 306–308 (2016)
Wang, J.: Design and implementation of test work platform based on Django. Chin. New Technol. New Prod. (2015)
Fan, J.: A survey on web application servers. Technol. Cent. Softw. Eng. 14, 1728–1739 (2003)
Rong, J.: Design and implementation of a deep web crawler. Comput. Modernization (3), 31–34 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ren, P., Liu, H., Li, F. (2020). Research on Friendvertising-Counter Technology in Big Data. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12487. Springer, Cham. https://doi.org/10.1007/978-3-030-62460-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-62460-6_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62459-0
Online ISBN: 978-3-030-62460-6
eBook Packages: Computer ScienceComputer Science (R0)