@inproceedings{
yao2024unveiling,
title={Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms},
author={Fan Yao and Yiming Liao and Jingzhou Liu and Shaoliang Nie and Qifan Wang and Haifeng Xu and Hongning Wang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=IGAN7RldcF}
}
On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as they compete for algorithmically allocated user traffic. This phenomenon subtly shapes the volume and diversity of the content pool, which is crucial for the platform's sustainability. In this work, we demonstrate, both theoretically and empirically, that a purely relevance-driven policy with low exploration strength boosts short-term user satisfaction but undermines the long-term richness of the content pool. In contrast, a more aggressive exploration policy may slightly compromise user satisfaction but promote higher content creation volume. Our findings reveal a fundamental trade-off between immediate user satisfaction and overall content production on UGC platforms. Building on this finding, we propose an efficient optimization method to identify the optimal exploration strength, balancing user and creator engagement. Our model can serve as a pre-deployment audit tool for recommendation algorithms on UGC platforms, helping to align their immediate objectives with sustainable, long-term goals.
Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms
Fan Yao Yiming Liao Jingzhou Liu Shaoliang Nie Qifan Wang Haifeng Xu Hongning Wang