Risk-Controlled Personalized Popularity Alignment for Frozen Collaborative Filtering

Popularity correction can improve agreement with user histories while reducing ranking utility. We study when to apply such edits to frozen recommenders under explicit utility-loss budgets. Our interface separates candidate construction, user ordering and risk authorization. Given the resulting candidate lists, independent calibration selects the largest score prefix whose positive-part harms for Recall and NDCG satisfy their respective budgets. Rejected edits return the original list exactly. Conformal risk control bounds marginal expected harm under exchangeability. We instantiate the interface with RCPA-CF, our risk-controlled popularity-alignment method, and compare CP and TTEN-style corrections. Evaluation covers three datasets, four backbones and five seeds, with disjoint calibration and evaluation users. Under the conservative RCPA setting, learned ordering reduces mean popularity calibration error (PCE) by 1.839%, versus 1.654% for gain-only and 1.626% for regret-only ordering. All three orderings meet both realized budgets in 56/60 runs; this is a batch diagnostic rather than a probability guarantee. Ordering gains concentrate in Gowalla BPR-MF and LightGCN. Historical calibration-pool replays show an additional benefit over a change-based heuristic in selected settings. Further analyses relate candidate choice and alignment metrics to retained benefit and user-level loss distributions. The interface thus enables selective popularity correction under explicit utility-loss budgets.