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- W2973201638 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)." @default.
- W2973201638 created "2019-09-19" @default.
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- W2973201638 date "2019-09-16" @default.
- W2973201638 modified "2023-10-18" @default.
- W2973201638 title "Recommendation System-based Upper Confidence Bound for Online Advertising" @default.
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