More Than a Match: State-Informed Attention for Unified Ranking

Unified ranking models use heterogeneous features and behavioral histories as evidence for predicting user feedback. Standard attention selects this evidence through contextualized query–key compatibility, expressing content matching and predictive usefulness through the same score. We introduce RISE (Ranking with Input-dependent State Enhancement), a plug-in attention module that provides an explicit score-level channel for contextual importance. RISE combines Amplitude Modulation (AM), which accumulates input-dependent decay controls to regulate auxiliary interaction strength, and Phase Alignment (PA), which accumulates input-dependent phase controls to adjust the relative orientation of low-dimensional auxiliary projections. The transformed projections produce a signed, query-specific correction to the original attention score, while preserving the backbone's native score-to-weight rule and value aggregation. Experiments across multiple backbones and datasets show that RISE improves average AUC over the corresponding baselines. These results support explicit contextual-importance modeling alongside the representations learned by ranking backbones.