How Xiaohongshu Leverages Large Models to Revolutionize Content Recommendation

This article details Xiaohongshu's multi‑stage recommendation pipeline—using massive multi‑modal pre‑training, long‑sequence modeling, real‑time context features, reinforcement learning and online deep learning—to precisely surface valuable content, address cold‑start challenges, and break information bubbles for billions of users.

Multimodal LearningReinforcement Learninglarge language model

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How Xiaohongshu Leverages Large Models to Revolutionize Content Recommendation

DaTaobao Tech

Sep 7, 2022 · Artificial Intelligence

Online Deep Learning (ODL) Model Optimization for Real‑Time Recommendation

The team enhanced real‑time recommendation by redesigning TensorFlow graphs—using constant‑folding, a custom CallGraphOP cache, a simplified dense layer, and CUDA‑Graph compatibility—boosting single‑machine throughput ~40%, raising GPU utilization from 30% to 43%, cutting latency and saving roughly 30% of hardware resources.

CUDA GraphGPU performanceModel Optimization

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Online Deep Learning (ODL) Model Optimization for Real‑Time Recommendation