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Recommendation System-based Upper Confidence Bound for Online Advertising
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abstract
In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for dealing with non-stationary and large-state spaces multi-armed bandit problems. The proposed method has been targeted to the problem of the product recommendation in the online advertising. Through extensive testing with RecoGym, an OpenAI Gym-based reinforcement learning environment for the product recommendation in online advertising, the proposed method outperforms the widespread reinforcement learning schemes such as $\epsilon$-Greedy, Upper Confidence (UCB1) and Exponential Weights for Exploration and Exploitation (EXP3).
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Cited by 2 Pith papers
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Data Acquisition for Improving Model Fairness using Reinforcement Learning
DataSift orders data-pool points by predicted effect on demographic parity and uses a bandit over partitions to acquire them, improving fairness with less data than baselines.
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From Theory to Practice with RAVEN-UCB: Addressing Non-Stationarity in Multi-Armed Bandits through Variance Adaptation
RAVEN-UCB proposes a variance-adaptive UCB algorithm for non-stationary bandits, but the proof of its main regret bound is mathematically invalid.
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