Issue |
E3S Web Conf.
Volume 235, 2021
2020 International Conference on New Energy Technology and Industrial Development (NETID 2020)
|
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Article Number | 03029 | |
Number of page(s) | 10 | |
Section | Analysis on the Development of Intelligent Supply Chain and Internet Digital Industrialization | |
DOI | https://doi.org/10.1051/e3sconf/202123503029 | |
Published online | 03 February 2021 |
Analysis on the “Douyin (Tiktok) Mania” Phenomenon Based on Recommendation Algorithms
School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China
* Corresponding author’s e-mail: zhaozhw3@mail2.sysu.edu.cn
As one of the most popular short video platform, Douyin has accumulated more than half of the Chinese netizens as its daily active users. Many users spend plenty of time viewing Douyin short videos, which make Douyin addiction become a widespread phenomenon. In this paper, the author analyzes algorithm principles used in Douyin. Combining both perspectives of mass media communication and algorithm technology to explain how it effects Douyin addiction. For one thing, the recommendation algorithm caters users by fully meeting their needs. Using the hierarchical interest label tree, the user persona and the partitioned data buckets strategy to recommend more accurate and personalized contents. For another, the algorithm uses the collaborative filtering algorithm and low-cost interaction design mechanism to make traps for users. The author also finds that there is a closed-loop relationship between Douyin addiction and algorithm optimization. The algorithm principles positively effects users’ continuance intention. Meanwhile, the more frequent the user uses Douyin, the more accurate the algorithm will be. If not intervened, the addiction may be severely exacerbated. So, the author comes up with a few suggestions for Douyin developers and users, trying to break the closed-loop.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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