most citedAccelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation

2 citations · 2 across the 1 of their papers we have counts for

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1 paper

cs.LG2021★ 2 cited

Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation

Haruka Kiyohara, Kosuke Kawakami, Yuta Saito

In recommender systems (RecSys) and real-time bidding (RTB) for online advertisements, we often try to optimize sequential decision making using bandit and reinforcement learning (…