Recommendation System-based Upper Confidence Bound for Online Advertising
Information Retrieval
2019-09-11 v1 Machine Learning
Machine Learning
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 -Greedy, Upper Confidence (UCB1) and Exponential Weights for Exploration and Exploitation (EXP3).
Cite
@article{arxiv.1909.04190,
title = {Recommendation System-based Upper Confidence Bound for Online Advertising},
author = {Nhan Nguyen-Thanh and Dana Marinca and Kinda Khawam and David Rohde and Flavian Vasile and Elena Simona Lohan and Steven Martin and Dominique Quadri},
journal= {arXiv preprint arXiv:1909.04190},
year = {2019}
}