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Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation
In RL-LTV, the critic studies historical trajectories of items and predict the future LTV of fresh item, while the actor suggests a score-based policy which maximizes the future LTV expectation.
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