SEMKOOPREC: SEMANTIC-CONTROLLED KOOPMAN DYNAMICS WITH CANDIDATE-PRESERVING RANKING CLOSURE

Sequential recommendation often entangles three roles: interaction history represents current behavior, the latest event redirects it, and ranking translates a forecast into discrete choices. Pretrained semantics usually remain static item features. We introduce a state–control–decision principle in which event-dependent semantic control drives contextual evolution and a bounded decision interface regulates its ranking effect. SEMKOOPREC realizes this principle with a frozen behavioral anchor, persistent–transient Koopman dynamics, multi-horizon representation closure, and candidate-preserving decision closure. Our analysis follows semantic-event perturbations through persistent and exponentially decaying responses to catalog-wide score error, yielding margin-certified candidate stability or a finite anchor-relative intervention bound. Under the comparison protocol shared by every method in Table 1, SEMKOOPREC achieves the best result on all 12 HR/NDCG metrics across three Amazon benchmarks, with HR@10 gains up to 36.50%. A separate strict history-masked evaluation yields positive gains over the matched anchor across five datasets and alternative semantic representations. Matched ablations support event control, persistent–transient evolution, and bounded decision closure, with fewer than 73K additional trainable parameters.