User History Reveals Residual Preference in Recommender Candidate Distributions

User histories can distinguish preferences among recommendation candidates that receive the same observed feedback, yet these distinctions may remain only partially expressed in a trained model's output. We study this residual user preference through history-guided refinement of a frozen candidate distribution under explicit probability-preservation constraints. We introduce \bench, a controlled benchmark combining real item catalogs from seven domains with programmatic user trajectories, private candidate-level utilities, and shared catalog features for generation and history scoring. We propose Selective Mass-Preserving History Projection (\method), which uses candidate-discriminative history scores to refine the teacher's conditional distribution within an eligible subset without additional policy training. The update preserves the subset's total probability mass and every outside-set probability, with exact recovery of the teacher distribution when intervention is rejected. Under the benchmark's feedback-conditioned protocol, \method improves GRPO's mean expected utility by $5.0\%$ on Test-IID and $5.9\%$ on Test-OOD relative to the frozen teacher. Across four teacher configurations, all 44 evaluated held-out domain–teacher–split cells have positive lower bounds in their pointwise 95% bootstrap intervals for the cluster-mean gain. In pooled diagnostics spanning Validation and both test splits, substituting another user's history reverses the mean gain from $+0.01997$ to $-0.01299$, connecting the improvement to user-specific historical evidence. These controlled results demonstrate recoverable preference in frozen recommendation distributions and, through comparison with unconstrained updates, reveal the utility cost of preserving designated candidate probabilities. Anonymous artifacts: https://anonymous.4open.science/r/ICLR_smphp-4C3D/.