Research on the Communication Strategy of Tik Tok Automobile Popular Science Account Based on Big Data
Authors: 
Dingyi Yuan, Fang Gu, Di Wang, Yuqi Li, Fei Xu, Jianshu LiAuthors Info & Claims
Dingyi Yuan, Fang Gu, Di Wang, Yuqi Li, Fei Xu, Jianshu LiAuthors Info & ClaimsPages 46 - 50
Abstract
The communication mode of combining popular science content with short video can break through the limitations of traditional text communication mode, and make knowledge easier to understand through video presentation, which meets the knowledge cognition needs of the audience. Therefore, short video of popular science has become an important carrier of knowledge communication at present. In order to meet the needs of the continuous development of enterprises, the marketing concept needs to keep pace with the times under the influence of the ever-changing external environment. This topic launches the research on the communication strategy of Tik Tok automobile popular science account based on big data. Establish personalized video recommendation system and algorithm based on big data analysis. The modeling process of users' interest is to extract and count the video information that users have watched in personalized recommendation system. According to the video content that the user browses daily, we can know the director type or video content type that the user likes. The research shows that compared with the user-based recommendation algorithm, the recommendation accuracy of the improved collaborative filtering algorithm has increased by 110.368%. The scheme proposed in this paper can upload videos to the network, providing users with more possibilities to express themselves.
References
[1]
Xie, L., Hang, F., Guo, W., Lv, Y., Ou, W., & Shibly, F. H. A. (2021). Network security defence system based on artificial intelligence and big data technology. International journal of high performance systems architecture, 2021(3/4), 10.
[2]
Zhang, Y., Chen, F., & Karl, R. (2022). Correction to: social media public opinion as flocks in a murmuration: conceptualizing and measuring opinion expression on social media. Journal of Computer-Mediated Communication, 2022(2), 2.
[3]
Sun, Q., & Wu, Q. (2022). Target similarity matching algorithm of big data in remote sensing image based on henon mapping. International Journal of Information and Communication Technology, 2022(1), 20.
[4]
Lohitha, S. N., & Kumar, P. M. (2022). An efficient framework for processing big data in internet of things enabled cloud environments. International journal of communication systems, 2022(10), 35.
[5]
Chhabra, Gurpal, Singh, Singh, Varinderpal, & Maninder. (2018). Hadoop-based analytic framework for cyber forensics. International Journal of Communication Systems, 31(15), 3772.1.
[6]
Wu, T., Song, K., Chen, Z., Zhao, H., & Yu, H. (2022). A software pam4 clock data recovery algorithm for high-speed serial communication. IET communications, 2022(4), 16.
[7]
Yu, D., Chen, R., & Chen, J. (2020). Video recommendation algorithm based on knowledge graph and collaborative filtering. International Journal of Performability Engineering, 2020(12), 16.
[8]
Zhang, F., Lu, Y., Chen, J., Liu, S., & Ling, Z. (2017). Robust collaborative filtering based on non-negative matrix factorization and r-1-norm. Knowledge-Based Systems, 118(15), 177-190.
[9]
Li, C., Li, Y., Wang, C., Dong, S., Gao, H., & Zhao, Ql. (2022). The multimedia recommendation algorithm based on probability graphical model. Multimedia tools and applications, 2022(14), 81.
[10]
Hammou, B. A., Lahcen, A. A., & Mouline, S. (2018). Apra: an approximate parallel recommendation algorithm for big data. Knowledge-Based Systems, 157(1), 10-19.
[11]
Chao, C., Cjw, A., Ty, B., Bing, H., & Fu, G. (2019). A 3d point cloud filtering algorithm based on surface variation factor classification - sciencedirect. Procedia Computer Science, 154(10), 54-61.
[12]
Verma, M., & Rawal, A. (2022). An enhanced item-based collaborative filtering approach for book recommender system design. ECS transactions, 2022(1), 107.
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Published In
November 2022
739 pages
ISBN:9781450396806
DOI:10.1145/3582935
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Published: 10 April 2023
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ICITEE 2022
ICITEE 2022: 5th International Conference on Information Technologies and Electrical Engineering
November 4 - 6, 2022
Changsha, China
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