Abstract
The application of new media technologies and artificial intelligence technologies has promoted the prosperity of the Internet. The connection between web users and media content resources is deepening. Studying the relationship between media content and web users has become our focus. In this paper, using the theory of complex network and the Agent theory, the attribute information of media content and web users are analyzed, the objects and object clusters are classified and defined, and the behavior mechanism of related objects is designed and analyzed to realize the intelligence of the relationship network. The classification and behavior mechanisms of the objects will provide theoretical premise for realizing the visual analysis of the relationship between media content and web users, and lay the foundation for further model design.
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References
Ning S., Qu X., Cai V., et al.: Clust-LDA: joint model for text mining and author group inference (2018)
Han, S., Qiao, Y., Zhang, Y.: Analyze users’ online shopping behavior using interconnected online interest-product network. In: IEEE Wireless Communications and Networking Conference, Barcelona, Spain, pp. 1–6 (2018)
Wan, S., Paris, C., Georgakopoulos, D.: Social media data aggregation and mining for internet-scale customer relationship management. In: 2015 IEEE International Conference on Information Reuse and Integration (IRI). IEEE (2015)
De Choudhury, M., et al.: Connecting content to community in social media via image content, user tags and user communication. In: IEEE International Conference on Multimedia & Expo. IEEE (2009)
Ma, J., et al.: Balancing user profile and social network structure for anchor link inferring across multiple online social networks. IEEE Access 5, 12031–12040 (2017)
Guoan, Y., Ting, X., Hao, C.: The definition and classification framework of network cluster behavior. J. People’s Public Secur. Univ. China (Soc. Sci. Ed.) 6, 99–104 (2010)
Zong, L., Gu, B.: Multi-agent modeling of network public opinion evolution in crisis communication environment. Inf. Sci. (9), 1414–1419 (2010)
Shan, L., Kun, H.: Research of content-user relationship based on intelligent tags in evolution network. In: 13th Conference on Image and Graphics Technologies and Applications (IGTA 2018), Beijing, China, pp. 566–577 (2018)
Marx, V.: The big challenges of big data. Nature 498(7453), 255–260 (2013)
Zhou, T., Zhang, Z., Chen, G.: The opportunities and challenges of complex networks research. J. Univ. Electron. Sci. Technol. China 43(1), 1–5 (2014)
Wooldridge, M., Jennings, N.R.: Intelligence agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1994)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20(20), 53–65 (1999)
Acknowledgments
The work of this paper was supported by the Fundamental Research Funds for the Central Universities and Scientific Research Grant of Asian Media Research Center at Communication University of China.
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Liu, S., Huang, K. (2019). Design and Analysis of Object Behavior in Media Content-User Relationship Network Model. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_3
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DOI: https://doi.org/10.1007/978-981-13-9917-6_3
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