The Investigation and Discussion Related to Recommendation Systems in Video Social Platforms

DOI

10.2991/978-94-6463-540-9_57How to use a DOI?

Keywords

Recommendation system; bilibili; tiktok; algorithm; AI

Abstract

With the increasing popularity of video social platforms, recommendation systems play a crucial role on these platforms. They can recommend content of interest to users based on their interests and preferences, greatly improving their content browsing experience. This article provides an in-depth analysis of the recommendation algorithms for the two popular video social platforms, Bilibili and TikTok. This study first introduced the key implementation steps of recommendation algorithms for these two platforms, including user behavior modeling, content feature extraction, and personalized recommendation. Through the analysis of existing algorithms, this study has identified some noteworthy issues, such as the possibility that overly precise personalized recommendations may cause the “information cocoon” effect and reduce the user’s content contact surface; Recommendation algorithms may have certain biases that affect the fairness of content creation; And issues such as user privacy protection. In response to these issues, it is possible to look forward to the future development direction of recommendation algorithms, including enhancing algorithm interpretability, building more fair recommendation systems, and adopting technologies such as federated learning and differential privacy to protect user privacy. Overall, this article provides a comprehensive analysis of the current status and future trends of recommendation algorithms on video social platforms, providing valuable references for research in this field.

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  - Rongxuan Zhang
PY  - 2024
DA  - 2024/10/16
TI  - The Investigation and Discussion Related to Recommendation Systems in Video Social Platforms
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 573
EP  - 579
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_57
DO  - 10.2991/978-94-6463-540-9_57
ID  - Zhang2024
ER  -