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Recommendation System-based Upper Confidence Bound for Online Advertising

2019-09-09 · Nhan Nguyen-Thanh, Dana Marinca, Kinda Khawam, David Rohde, Flavian vasile, Elena Simona Lohan, Steven Martin, Dominique Quadri

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).

📄 PDF Abstract BibTeX arXiv:1909.04190

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OpenAI Gym Product Recommendation reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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