A Strategy for Advertisement Placement based on the Multi-Armed Tiger Problem

Abstract

The purpose of this study is to investigate the effectiveness of using Multi armed bandit model, which contains -greedy, Upper Confidence Bound (UCB), and Thompson sampling algorithms, to optimize online advertisement placement. Through simulating different types of ad placements using different algorithms and comparing them, this paper intends to demonstrate the feasibility of the Multi-Armed Robber model for the ad placement problem. The results show that the multi-armed bandit model can improve the ad click rate compared with the traditional ad placement strategy. Thompson sampling algorithm outperforms the -greedy algorithm and UCB algorithm in this paper's experiments, which can better balance exploration and application and reduce regret. The algorithm provides a more efficient method of allocating ad resources. These findings provide new insights into the field of digital marketing and may have an impact on the development of actual ad placement strategies.