In large-scale recommendation scenarios, achieving high-precision ranking requires simultaneously modeling user interest dynamics and content propagation potential. In this work, we propose a unified framework that integrates a temporal interest modeling stream with a multimodal virality encoder. The temporal stream captures sequential user behavior through the self-attention-based modeling of long-term and short-term interests, while the virality encoder learns latent virality factors from heterogeneous modalities, including text, images, audio, and user comments. The two streams are fused in the ranking layer to form a joint representation that balances personalized preference with content dissemination potential. To further enhance efficiency, we design hierarchical cascade heads with gating recursion for progressive refinement, along with a multi-level pruning and cache management strategy that reduces redundancy during inference. Experiments on three real-world datasets (Douyin, Bilibili, and MIND) demonstrate that our method achieves significant improvements over state-of-the-art baselines across multiple metrics. Additional analyses confirm the interpretability of the virality factors and highlight their positive correlation with real-world popularity indicators. These results validate the effectiveness and practicality of our approach for high-precision recommendation in big data environments.