- Adaptive recommendations: build a system that changes with user feedback over time.
- Beyond immediate clicks: traditional methods often optimize a short metric (like clicks) and ignore long-term goals such as retention or lifetime value (LTV).
- Why RL: Reinforcement Learning can learn policies that aim for long- term objectives by using reward signals that represent business goals.
- Dynamic adaptation: RL updates its policy as new user feedback arrives.
- Long-term optimization: RL can use reward functions (retention, revenue, LTV) instead of only maximizing instant clicks.
- Exploitation: show popular items that most users like to get good immediate engagement.
- Exploration: occasionally show new or rare items to learn user preferences.
- Balance: a good trade-off helps discover user interests and raises long-term engagement and value.
- MovieLens
- Amazon Product Data
- Contextual Bandits — a simple, practical approach for managing exploration vs exploitation. Good starting point.
- DQN (Deep Q-Network) — a more powerful RL method that can learn long-term value through function approximation.
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