The Context Trap: Addressing Item Neglect and Calibration in Deep Point-Wise Rankers
Most ML practitioners working on ranking have had this experience: the model looks good on paper - AUC, precision are all healthy - but something feels off when the scores drive real decisions.
The underlying cause is often feature neglect. In supervised pointwise ranking, where a model predicts a relevance score for each item-context pair independently, learners frequently over-rely on contextual signals at the expense of item-specific features. This may yield decent classification metrics, but it leads to poorly calibrated scores - and in practice, we rarely stop at ranking. Model outputs feed downstream tasks: thresholds, priority queues, risk tiers, decision triggers. That's where miscalibration stops being a metric problem and becomes a decision-making problem.
In this talk, we present architectural interventions to address feature neglect directly: item-aware attention mechanisms, cross-feature interactions, and feature representation enhancements that explicitly strengthen item-context relationships. Through real-world experiments, we show these changes not only improve ranking performance but also produce better-calibrated scores - outputs you can trust beyond the leaderboard.
The talk is targeted at Data/ML Scientists and ML Engineers working on recommendation or ranking systems who want to move towards more calibrated and robust ranking architectures.