The content introduces the Cascading Reinforcement Learning framework, emphasizing the importance of considering user states in recommendation systems. It proposes efficient algorithms, such as CascadingVI and CascadingBPI, to optimize item selection for maximizing cumulative rewards. The study highlights the computational challenges and solutions in handling combinatorial action spaces efficiently.
The proposed framework extends traditional cascading bandit models by incorporating user states and state transitions into decision-making processes. By leveraging an oracle BestPerm and exploring properties of value functions, the algorithms developed demonstrate improved computational efficiency compared to existing RL adaptations. Experimental results showcase the effectiveness of the proposed algorithms in terms of regret minimization and sample complexity guarantees.
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by Yihan Du,R. ... at arxiv.org 03-13-2024
https://arxiv.org/pdf/2401.08961.pdfDeeper Inquiries