Reinforcement Learning for Recommendations and Search
Eugene Yan
Recommendation systems are adopting reinforcement learning methods including contextual bandits, value-based approaches like deep Q-networks, and policy-based methods to optimize for long-term user engagement rather than immediate clicks and to balance exploration of new items with exploitation of popular ones. Companies including Yahoo, Netflix, JD, Microsoft, ByteDance, and Google have deployed these RL techniques, with specific examples including contextual bandits for news article recommendations using 1,193 user features and 83 article features, and DQNs that incorporate both positive and negative user feedback to improve ranking. These approaches enable continuous online learning, reduce cold-start problems for new items, and allow systems to consider long-term metrics like user retention rather than optimizing solely for immediate user actions.
Why it matters
Focusing on long-term rewards, exploration, and frequently updated item.
