  A key puzzle in search, ads, and recommendation is that the ranking model can
only utilize a small portion of the vastly available user interaction data. As
a result, increasing data volume, model size, or computation FLOPs will quickly
suffer from diminishing returns. We examined this problem and found that one of
the root causes may lie in the so-called ``item-centric'' formulation, which
has an unbounded vocabulary and thus uncontrolled model complexity. To mitigate
quality saturation, we introduce an alternative formulation named
``user-centric ranking'', which is based on a transposed view of the dyadic
user-item interaction data. We show that this formulation has a promising
scaling property, enabling us to train better-converged models on substantially
larger data sets.
