R-HIBR: RELIABILITY-CONSTRAINED EVIDENCE REVISION FOR SEQUENTIAL RECOMMENDATION

When should auxiliary evidence be allowed to revise a contextual recommendation, and by how much? We study this question as reliability-constrained evidence revision. R-HIBR retains a semantic-initialized contextual anchor and constructs transition and historical-semantic evidence whose score contributions reflect support and behavioral relevance. Bayesian posterior-predictive lift measures successor evidence beyond catalog prevalence, while deterministic support attenuation controls its multi-horizon contribution. Training transition discrimination calibrates historical semantics, and a persistence probe informs bounded contextual routing. The resulting analysis links evidence estimation to a score-space intervention budget: sufficiently separated rankings are preserved, a pairwise evidence advantage can overcome an anchor deficit, and jointly vanishing corrections recover the anchor. Calibration perturbations are bounded through both revision paths. Under matched full-catalog evaluation, R-HIBR leads all 12 Amazon HR/NDCG metrics and all four OnlineRetail metrics. Five-dataset ablations, 45 sensitivity evaluations, and a four-encoder transfer study characterize how evidence construction and intervention control contribute to the final ranking.