Prediction–Ranking Trade-Offs in Latent-Factor Recommenders: An Uncertainty-Aware Selection Framework

Abstract

When numerical predictions and ranked recommendations have task value, selection must expose their trade-offs. We examine selection consequences and incremental uncertainty value in biased matrix factorization. Across 35 configurations and four training-evidence levels on MovieLens 1M, CiaoDVD and Yelp–Tampa, validation root mean squared error (RMSE) and normalized discounted cumulative gain for the top ten recommendations (NDCG@10) select different configurations in eleven of twelve conditions. Fresh-seed confirmation shows positive ranking regret whenever selections differ, but only four intervals lie wholly above the declared absolute tolerance of 0.005. An uncertainty-aware (UA) rule minimizes RMSE subject to a paired-seed upper bound on validation ranking regret. A revision-stage mean-regret ablation removes only the uncertainty penalty. At 0.005 tolerance, choices differ in seven of sixteen conditions, and UA improves held-out ranking point estimates in six at higher global RMSE, with no uniform improvement across both endpoints. Four-margin reselection and validation-user resampling expose sensitivity. In the frozen prospective Epinions evaluation, 35 candidates are admissible and UA equals RMSE selection. All four absolute-margin tests pass, yet ranking losses represent 28.2–62.2% of reference NDCG. Expanded searches retain objective disagreement and finite budget limits. The results characterize conditional trade-offs, not universal superiority, so an absolute tolerance can constrain decisions without ensuring proportional ranking preservation or deployment utility.