Personalized Recommendation Algorithms on Short Video Platforms: User Experience, Ethical Concerns, and Social Impact

Research Article
Open access

Personalized Recommendation Algorithms on Short Video Platforms: User Experience, Ethical Concerns, and Social Impact

Siyuan Yin 1*
  • 1 New College, University of Toronto, Toronto, Canada    
  • *corresponding author siyuan.yin@mail.utoronto.ca
LNEP Vol.90
ISSN (Print): 2753-7056
ISSN (Online): 2753-7048
ISBN (Print): 978-1-80590-085-6
ISBN (Online): 978-1-80590-086-3

Abstract

This paper discusses how algorithmic personalisation is being utilised among short video platforms, with an emphasis on Douyin in particular. In this paper, I integrate an in-depth literature review and original survey evidence to explore how personalised recommendation algorithms affect user engagement, content exposure, and perceptions of fairness and privacy. The results indicate that such algorithms significantly increase user screen time and influence opinion formation, while also leading to repeated exposure to similar content, the emergence of filter bubbles, and unequal visibility for less popular creators. Although many individuals report high awareness of how these systems work, their ability to meaningfully control or adjust algorithmic outputs remains limited, and concerns around data privacy are widespread. This paper highlights a central irony: while algorithmic media enhance satisfaction and platform retention, they simultaneously pose serious ethical challenges. This work concludes by emphasising the need for greater algorithmic transparency, stronger user agency, and fairness-focused design to ensure more accountable and inclusive digital media environments.

Keywords:

Personalized Recommendation, Short Video Platforms, Algorithmic Ethics

Yin,S. (2025). Personalized Recommendation Algorithms on Short Video Platforms: User Experience, Ethical Concerns, and Social Impact. Lecture Notes in Education Psychology and Public Media,90,89-93.
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References

[1]. Zhang, R. (2024). The investigation and discussion related to recommendation systems in video social platforms. Advances in Computer Science Research, 573–579. https://doi.org/10.2991/978-94-6463-540-9_57

[2]. Guo, Y., Wang, M., & Li, X. (2017). An interactive personalized recommendation system using the hybrid algorithm model. Symmetry, 9(10), 216. https://doi.org/10.3390/sym9100216

[3]. Guy, I., Zwerdling, N., Ronen, I., Carmel, D., & Uziel, E. (2010). Social media recommendation based on people and tags. Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/1835449.1835484

[4]. Anandhan, A., Shuib, L., Ismail, M. A., & Mujtaba, G. (2018). Social Media Recommender Systems: Review and open research issues. IEEE Access, 6, 15608–15628. https://doi.org/10.1109/access.2018.2810062

[5]. Cosse, C. (2024). Recommendation systems of short video platforms: Auditing algorithms of short format video platforms to understand the rabbit hole effect on YouTube Shorts. Delft University of Technology Master Thesis.

[6]. Kim, Sang Ah. (2017). Social media algorithms: why you see what you see. Georgetown Law Technology Review, 2(1), 147-154. https://heinonline.org/HOL/Page?handle=hein.journals/gtltr2&div=12&g_sent=1&casa_token=XITJcllkg7cAAAAA:1GHsoep1i2TB1UAwO25uNkId3D20cpe4gJKjLzaNem0irHNMfhi_LXkh4vNBPUrTemFVbU4joxU&collection=journals

[7]. Peralta, A. F., Kertész, J., & Iñiguez, G. (2021). Opinion formation on social networks with algorithmic bias: Dynamics and bias imbalance. Journal of Physics: Complexity, 2(4), 045009. https://doi.org/10.1088/2632-072x/ac340f

[8]. Shin, D., Hameleers, M., Park, Y. J., Kim, J. N., Trielli, D., Diakopoulos, N., Helberger, N., Lewis, S. C., Westlund, O., & Baumann, S. (2022). Countering algorithmic bias and disinformation and effectively harnessing the power of AI in media. Journalism & Mass Communication Quarterly, 99(4), 887–907. https://doi.org/10.1177/10776990221129245

[9]. Figueiredo, C., & Bolaño, C. (2017). Social Media and algorithms: Configurations of the lifeworld colonization by new media. The International Review of Information Ethics, 26. https://doi.org/10.29173/irie277

Cite this article

Yin,S. (2025). Personalized Recommendation Algorithms on Short Video Platforms: User Experience, Ethical Concerns, and Social Impact. Lecture Notes in Education Psychology and Public Media,90,89-93.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Global Politics and Socio-Humanities

ISBN：978-1-80590-085-6(Print) / 978-1-80590-086-3(Online)
Editor：Enrique Mallen
Conference website: https://2025.icgpsh.org/
Conference date: 25 July 2025
Series: Lecture Notes in Education Psychology and Public Media
Volume number: Vol.90
ISSN：2753-7048(Print) / 2753-7056(Online)

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References

[1]. Zhang, R. (2024). The investigation and discussion related to recommendation systems in video social platforms. Advances in Computer Science Research, 573–579. https://doi.org/10.2991/978-94-6463-540-9_57

[2]. Guo, Y., Wang, M., & Li, X. (2017). An interactive personalized recommendation system using the hybrid algorithm model. Symmetry, 9(10), 216. https://doi.org/10.3390/sym9100216

[3]. Guy, I., Zwerdling, N., Ronen, I., Carmel, D., & Uziel, E. (2010). Social media recommendation based on people and tags. Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/1835449.1835484

[4]. Anandhan, A., Shuib, L., Ismail, M. A., & Mujtaba, G. (2018). Social Media Recommender Systems: Review and open research issues. IEEE Access, 6, 15608–15628. https://doi.org/10.1109/access.2018.2810062

[5]. Cosse, C. (2024). Recommendation systems of short video platforms: Auditing algorithms of short format video platforms to understand the rabbit hole effect on YouTube Shorts. Delft University of Technology Master Thesis.

[6]. Kim, Sang Ah. (2017). Social media algorithms: why you see what you see. Georgetown Law Technology Review, 2(1), 147-154. https://heinonline.org/HOL/Page?handle=hein.journals/gtltr2&div=12&g_sent=1&casa_token=XITJcllkg7cAAAAA:1GHsoep1i2TB1UAwO25uNkId3D20cpe4gJKjLzaNem0irHNMfhi_LXkh4vNBPUrTemFVbU4joxU&collection=journals

[7]. Peralta, A. F., Kertész, J., & Iñiguez, G. (2021). Opinion formation on social networks with algorithmic bias: Dynamics and bias imbalance. Journal of Physics: Complexity, 2(4), 045009. https://doi.org/10.1088/2632-072x/ac340f

[8]. Shin, D., Hameleers, M., Park, Y. J., Kim, J. N., Trielli, D., Diakopoulos, N., Helberger, N., Lewis, S. C., Westlund, O., & Baumann, S. (2022). Countering algorithmic bias and disinformation and effectively harnessing the power of AI in media. Journalism & Mass Communication Quarterly, 99(4), 887–907. https://doi.org/10.1177/10776990221129245

[9]. Figueiredo, C., & Bolaño, C. (2017). Social Media and algorithms: Configurations of the lifeworld colonization by new media. The International Review of Information Ethics, 26. https://doi.org/10.29173/irie277

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