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This presentation details how JD applied reinforcement learning—using RNN‑based user state modeling and a DDPG framework—to improve e‑commerce search ranking by optimizing long‑term cumulative value, showing significant offline and online gains in conversion and GMV.
This article explains how reinforcement learning, specifically the DDPG algorithm combined with Transformer-based networks, is applied to improve large‑scale news recall systems, detailing the business scenario, algorithm selection, model architecture, speed optimizations, training challenges, and observed online performance gains.
This article reviews reinforcement learning fundamentals, contrasts it with supervised learning, surveys major RL algorithms such as DDPG and DQN, and details how these methods can be modeled for sequential news recommendation, including system architecture, state‑action definitions, and practical challenges.