Recommendation System-based Upper Confidence Bound for Online Advertising
This addresses the challenge of improving product recommendations in online advertising, but it appears incremental as it builds on existing UCB and recommendation system techniques.
The authors tackled the problem of product recommendation in online advertising by proposing UCB-RS, a method that enhances the UCB algorithm using recommendation systems to handle non-stationary and large-state spaces in multi-armed bandit problems, and it outperformed methods like ε-Greedy, UCB1, and EXP3 in tests with the RecoGym environment.
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 $ε$-Greedy, Upper Confidence (UCB1) and Exponential Weights for Exploration and Exploitation (EXP3).