The Next Recommendation Is a Direction: Discovering High-Value Semantic Opportunities Beyond User History

Recommenders usually choose the next item, but interest exploration and generative recommendation raise an earlier question: where in the content space should the system go next? We formulate next semantic direction selection: given a short user history, find a continuous semantic location whose nearby, previously unobserved content contains a high-value realization. Our hypothesis is that population feedback contains cross-content behavioral regularities that transfer information from observed responses to semantically different content. We introduce NORTH, a population-anchored framework for continuous direction search. Shared feedback learns behavioral representations; the user's history turns population opportunity into a personalized direction score. A learned policy starts from promising population anchors, decides where to search and whether to stay or move, and proposes continuous displacements. Each proposal is grounded in a fixed-size core of real items and assessed by the same direction scorer. Best-Realization Value measures the best observed response within this fixed realization budget. On the full and fully-observed dataset variants, KuaiRec-Full and KuaiRec-FO, NORTH exceeds population-only bank selection in all four evaluated settings. It leads the compared OPEN generators on KuaiRec-Full and KuaiRec-FO. Ablations support the use of history-conditioned proposals and multimodal displacements.