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).
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02282575
Contributor : Nhan Nguyen-Thanh <>
Submitted on : Tuesday, September 10, 2019 - 10:21:29 AM
Last modification on : Wednesday, December 11, 2019 - 1:24:46 AM

File

RSUCB.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02282575, version 1

Citation

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⟩

Share

Metrics

Record views

59

Files downloads

64