  Driven by the new economic opportunities created by the creator economy, an
increasing number of content creators rely on and compete for revenue generated
from online content recommendation platforms. This burgeoning competition
reshapes the dynamics of content distribution and profoundly impacts long-term
user welfare on the platform. However, the absence of a comprehensive picture
of global user preference distribution often traps the competition, especially
the creators, in states that yield sub-optimal user welfare. To encourage
creators to best serve a broad user population with relevant content, it
becomes the platform's responsibility to leverage its information advantage
regarding user preference distribution to accurately signal creators.
  In this study, we perform system-side user welfare optimization under a
competitive game setting among content creators. We propose an algorithmic
solution for the platform, which dynamically computes a sequence of weights for
each user based on their satisfaction of the recommended content. These weights
are then utilized to design mechanisms that adjust the recommendation policy or
the post-recommendation rewards, thereby influencing creators' content
production strategies. To validate the effectiveness of our proposed method, we
report our findings from a series of experiments, including: 1. a
proof-of-concept negative example illustrating how creators' strategies
converge towards sub-optimal states without platform intervention; 2. offline
experiments employing our proposed intervention mechanisms on diverse datasets;
and 3. results from a three-week online experiment conducted on a leading
short-video recommendation platform.
