  The past decade has witnessed the flourishing of a new profession as media
content creators, who rely on revenue streams from online content
recommendation platforms. The reward mechanism employed by these platforms
creates a competitive environment among creators which affect their production
choices and, consequently, content distribution and system welfare. It is thus
crucial to design the platform's reward mechanism in order to steer the
creators' competition towards a desirable welfare outcome in the long run. This
work makes two major contributions in this regard: first, we uncover a
fundamental limit about a class of widely adopted mechanisms, coined
Merit-based Monotone Mechanisms, by showing that they inevitably lead to a
constant fraction loss of the optimal welfare. To circumvent this limitation,
we introduce Backward Rewarding Mechanisms (BRMs) and show that the competition
game resultant from BRMs possesses a potential game structure. BRMs thus
naturally induce strategic creators' collective behaviors towards optimizing
the potential function, which can be designed to match any given welfare
metric. In addition, the BRM class can be parameterized to allow the platform
to directly optimize welfare within the feasible mechanism space even when the
welfare metric is not explicitly defined.
