Recommendation System-based Upper Confidence Bound for Online Advertising

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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Contributor : Nhan Nguyen-Thanh <>
Submitted on : Tuesday, September 10, 2019 - 10:21:29 AM
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  • HAL Id : hal-02282575, version 1


Nhan Nguyen-Thanh, Dana Marinca, Kinda Khawam, David Rohde, Flavian Vasile, et al.. Recommendation System-based Upper Confidence Bound for Online Advertising. REVEAL 2019, Sep 2019, Copenhagen, Denmark. ⟨hal-02282575⟩



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