Anticipating and capturing transient demand spikes is a critical challenge for e-commerce platforms, as reactive discovery mechanisms often fail to surface relevant products during rapid cultural or seasonal shifts. We propose TrendPulse, a three-stage framework that identifies regional search momentum, leverages Large Language Model (LLM) to transform spikes into semantic trends, and employs a cross-attention mechanism to provide personalized catalog recommendations. Our comprehensive ablation experiments and evaluations validate the impact of each architectural component, showing consistent improvements across multiple critical business metrics. TrendPulse’s effectiveness is further validated through online A/B experiments, where it drives measurable gains in both business metrics and overall user experience. Finally, we outlined the deployment strategy in detail, providing a reproducible blueprint that can be readily applied to similar industry-scale applications.

Accurately inferring implicit physical attributes of products, such as weight, is critical for large-scale e-commerce logistics but challenging due to sparse or unreliable textual metadata and high visual variability. We formulate weight estimation as a grounded multimodal reasoning problem and investigate whether large vision-language models (LVLMs) can infer discretized weight buckets through in-context learning (ICL) over product images and descriptions. We introduce a scalable inference framework that conditions predictions on automatically retrieved, category-specific exemplars and propose a distribution-calibrated retrieval strategy that aligns few-shot contexts with the empirical weight distribution of each product sub-category. This calibration substantially improves few-shot multimodal reasoning compared to random or embedding-based retrieval baselines. Across 14 high-variance categories, our approach significantly outperforms strong multimodal KNN baselines in both exact-match accuracy and near-bucket reliability. Deployed in production on a large e-commerce platform, our system processes millions of listings daily and reduces shipping-related revenue leakage by ∼22%, demonstrating that multimodal ICL can serve as a practical and cost-effective alternative to manual or hardware-based verification.