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A key puzzle in search, ads, and recommendation is that the ranking model canonly utilize a small portion of the vastly available user interaction data. Asa result, increasing data volume, model size, or computation FLOPs will quicklysuffer from diminishing returns. We examined this problem and found that one ofthe root causes may lie in the so-called ``item-centric'' formulation, whichhas an unbounded vocabulary and thus uncontrolled model complexity. To mitigatequality saturation, we introduce an alternative formulation named``user-centric ranking'', which is based on a transposed view of the dyadicuser-item interaction data. We show that this formulation has a promisingscaling property, enabling us to train better-converged models on substantiallylarger data sets.

Breaking the Curse of Quality Saturation with User-Centric Ranking