Modern recommender systems rely on black-box machine learning models to predict consumer choices. However, because these models do not explicitly represent the underlying data-generating process (DGP), they often struggle to generalize beyond observed data. A growing body of work advocates for incorporating consumer intent into personalization systems to improve generalization. Yet in the context of landing page recommendations—the most common and challenging personalization setting–a list of recommendations must be generated immediately when a consumer enters the platform, before any explicit intent signal is available. We propose the Intent-Structured Landing-Page Recommender System (ISRec), a principled framework that incorporates intent-based structure into multi-stage landing page recommender systems without requiring explicit consumer input and while satisfying industrial latency constraints. ISRec defines intent as a consumer’s dynamic receptiveness to subsets of content, allowing intent labels to be inferred directly from observed behavior. It consists of three stages: intent prediction, intent-aware reward modeling, and intent-aware whole-page optimization, serving as the optimal greedy solution to the original NP-hard intent-aware recommendation problem with provable regret-bound guarantees. We evaluate ISRec on YouTube, the world’s largest video recommendation platform, and find that it significantly improves daily active users (DAU) by 0.05% and overall user enjoyment by 0.09%, among the largest business metric gains observed in recent YouTube experiments, corresponding to an estimated $32.5 million in annual ads revenue. Our findings provide empirical evidence that even without knowing the true DGP, enforcing a partial structure aligned with it can help the model generalize. Managerially, ISRec offers a principled and generalizable framework for integrating behavioral and structural insights into real-time industrial personalization systems, paving the way for human-in-the-loop AI design.