China’s two leading short-video platforms share a surface similarity but pursue distinct strategies. Kuaishou emphasizes grassroots creators and trust-based commerce in lower-tier communities, with revenues spanning ads, livestream e-commerce, and gifting. Douyin (TikTok’s Chinese sibling) leans on entertainment scale and ad performance, layering in rapid e-commerce growth (Tang et al., 2022).
These choices surface in ranking design. Kuaishou invests in long-term user health, using reinforcement-learning objectives tied to retention which is an explicit shift from short-term clicks to durable engagement (Cai et al., 2023). In parallel, the fair-ranking literature shows why platforms that monitor exposure inequality can mitigate winner-take-all dynamics among creators (Biega, Gummadi, & Weikum, 2018; Do & Usunier, 2022).
Douyin, by contrast, publicly emphasizes a highly personalized “For You” recommender that scales content based on interaction signals (watch time, shares, follows), now with added transparency controls which is a classic accelerator design (Feng, 2025).
The theory of two-sided markets suggests both models are rational: unchecked same-side effects like creator crowd-out can weaken ecosystems, so platforms must govern distribution to keep value flowing (Eisenmann et al., 2006; Van Alstyne et al., 2016). Kuaishou builds the brakes into its ranking, while Douyin optimizes for speed and patches with policy.
In my view, Kuaishou’s approach feels more sustainable. By ensuring smaller creators gain visibility, it fosters loyalty and builds a broader base for commerce. It reflects a deliberate choice: growth through fairness. Douyin’s accelerator excels at grabbing attention, but the risk is burnout. That could be the case for both users tired of repetitive content and creators crowded out of discovery.
If forced to bet on the long game, I’d pick the algorithm and business model of Kuaishou. Scale delivers quick wins, but healthier network effects come from balancing growth with inclusion which may prove to be Kuaishou’s hidden advantage.
References
Biega, A. J., Gummadi, K. P., & Weikum, G. (2018). Equity of attention: Amortizing individual fairness in rankings. Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, 405–414. https://doi.org/10.1145/3209978.3210063
Cai, Q., Liu, S., Wang, X., Zuo, T., Xie, W., Yang, B., Zheng, D., Jiang, P., & Gai, K. (2023). Reinforcing user retention in a billion scale short video recommender system. Proceedings of the ACM Web Conference 2023, 1273–1282.https://doi.org/10.1145/3543873.3584640
Do, V., & Usunier, N. (2022). Optimizing generalized Gini indices for fairness in rankings. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 737–747.https://doi.org/10.1145/3477495.3532035
Eisenmann, T. R., Parker, G., & Van Alstyne, M. W. (2006). Strategies for two-sided markets. Harvard Business Review, 84(10), 92–101.
Feng, C. (2025, April 2). ByteDance-owned Douyin sheds light on recommendation algorithm amid regulatory pressure. South China Morning Post. https://www.scmp.com/tech/big-tech/article/3304799/bytedance-owned-douyin-sheds-lights-recommendation-algorithm-amid-regulatory-pressure
Van Alstyne, M. W., Parker, G. G., & Choudary, S. P. (2016). Pipelines, platforms, and the new rules of strategy. Harvard Business Review, 94(4), 54–60, 62.
