RIGC: A Recommendation-Informed Foundation Model for Personalized Multimodal Content Generation

In real-world recommender systems, generating personalized multimodal content remains largely unexplored, and the readily available recipe is a cascaded pipeline: a recommendation model compresses user behavior into an intermediate representation, on which a separate frozen generator then conditions. Such a bottleneck interface is fundamentally lossy, as the user is invisible to the generator once compressed, and personalization reduces to rendering a static description. Closing this gap calls for an end-to-end structure where recommendation and content generation share one model, one representation. We therefore present RIGC (Recommendation-Informed Generated Content), a foundation model realizing this design within a single mixture-of-transformers backbone, where user information persists in the shared attention context and flows losslessly into generation. Such unification is non-trivial, as the backbone must reconcile heterogeneous objectives spanning item retrieval, text generation, and image synthesis. To this end, we construct a large-scale industrial multimodal dataset pairing user behavior with item content, and design a multi-stage alignment curriculum that progressively instills recommendation capability into a native multimodal generative model. Aligning with real user preferences further poses a cross-modal credit assignment problem, as a single feedback signal entangles all modalities, for which we introduce Nested-GRPO to sample hierarchical rollouts per modality and credit each by its marginal advantage. Experiments on public and industrial benchmarks show that RIGC surpasses dedicated recommenders in retrieval accuracy and consistently outperforms cascaded and unified baselines in generation fidelity, quality, and personalization, with Nested-GRPO yielding further gains in conversion and user specificity. These results indicate that recommendation and generation are mutually reinforcing over a shared representation, positioning unified generative recommendation as a foundation for personalized content delivery.