Research on Douyin Data Analysis Based on Deep Learning

DOI

10.2991/978-94-6463-562-1_22How to use a DOI?

Keywords

Deep learning algorithms; Douyin; data mining; User profile

Abstract

There are many types of data on the Douyin platform, including user behavior data, video content data, interaction data, etc., and the amount of data is huge. Deep learning technology, with its powerful data processing capabilities, is able to efficiently process these complex data and extract valuable information and features. By analyzing Douyin data, you can gain insight into users’ interests, behaviors, preferences, and other characteristics, so as to help brands or creators target audiences more accurately. This precise targeting helps to improve the pertinence and attractiveness of the content, increasing user stickiness and conversion rates. And analytics can reveal what types of content are more popular, when content is more effective, how users interact with it, and more. This information is essential for optimizing content strategies, which can help creators adjust the direction, format, and release time of content to better meet user needs and improve the dissemination and influence of content. So as to improve the marketing effect and enhance competitiveness.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yuxia Du
AU  - Joey S. Aviles
PY  - 2024
DA  - 2024/11/13
TI  - Research on Douyin Data Analysis Based on Deep Learning
BT  - Proceedings of the 2024 5th International Conference on Big Data and Social Sciences (ICBDSS 2024)
PB  - Atlantis Press
SP  - 232
EP  - 240
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-562-1_22
DO  - 10.2991/978-94-6463-562-1_22
ID  - Du2024
ER  -