Efficient Conformal Calibration for Paired Recommendation Decisions
Xinyu Qiao, Yichen Lin, Zhewen Chen, Xue Wang, Tao Yao
Abstract
Risk control can suppress useful changes to a frozen recommendation rule. We study how to recover valid activation from the same observed fractional losses. Two specified internal calibrators can lose efficiency by compressing losses into their sum or clipping auxiliary inverse counts before averaging. We propose Averaged Conformal Risk Calibration, or ACRC, which averages before clipping. A product identity proves expected-risk validity, while pointwise bounds characterize the additional activation. For fixed populations sampled without replacement, we connect activation to signed utility under heterogeneous users and switching constraints. Near the budgets, positive population mean gain and the stated nondegeneracy conditions give positive leading-order utility increments despite sampling dependence. Across six datasets, changing only calibration improves discounted net utility by $0.010226$ over raw and $0.008337$ over RB. A separately frozen Jester confirmation gives $0.011025 \pm 0.001082$ over raw with the rule and filter fixed, using a five-seed approximate 95 percent paired interval. Against information-matched SCoRE selecting risk or reward scores, the mean utility difference is $+0.015934 \pm 0.001440$. These gains use more mean realized risk within the declared budgets. Upstream failures delimit the benefit: efficient calibration releases useful modifications when the proposals themselves have value.