DART - Drift Aware Ranking with forecasTing and uncertainty

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Ranking systems in online platforms across domains must handle temporal drift where artifact distributions and contextual relevance evolve over time. Existing approaches address temporal drift using recency weighting, sliding windows, or forecasting techniques, however, they do not focus on integrating drift signals with ranking systems or handle the data variability introduced by the drift. We propose DART, a two-stage architecture combining a ranking model with a forecasting model that predicts artifact performance for a look-ahead window. An uncertainty-aware attention mechanism dynamically weights forecasted information based on prediction confidence, enabling robust adaptation without online updates to the ranker at serving time. Prior work uses uncertainty either to detect drift [19] or to debias ranking [11]; DART instead uses it as a control signal that modulates information flow from a forecaster to the ranker. Experiments on a large-scale e-commerce promotion ranking dataset show DART’s deployable offline configuration achieves +11.11% ± 0.11% relative Mean Reciprocal Rank (MRR) during promotional events and +7.37% ± 0.11% pre-event. On the public Coveo SIGIR dataset DART delivers ∼ +22% MRR in both high and low-drift periods, showing dual-regime robustness across abrupt and gradual drift; the mechanism is task-agnostic and also improves a regression benchmark. Ablations demonstrate that attention-based integration of forecasted information substantially outperforms direct feature inclusion, reinforcing the importance of uncertainty-aware temporal adaptation in ranking systems.