Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on Marketing Dataset

Abstract

Background, A/B checking is a regular measure in many marketing procedures for ecommerce companies. Through well-designed A/B research, advertisers can gain insight about when and how marketing efforts can be maximized and active promotions driven. In practical terms, standard A/B experimentation makes less money relative to more advanced machine learning methods. Purposes, in order to examine the current A/B testing state, identify some popular machine learning algorithms (multi-arm bandits) which are used to optimize A/B testing, and then explain the output in some standard marketing cases of these algorithms. Methodology, In this study, the state of A/B testing have been addressed, some typical A/B learning algorithms (Multi-Arms Bandits) like Thompson Sampling, Epsilon Greedy and UCB-1 will be implemented and compared used to optimize A/B testing are described and comparable. As a result, UCB-1 and Thompson Sampling, be an exceptional winner to optimize payouts in this situation. Because it showed more effective results, without losing experimentation and statistical variations, to maximize total payouts. Based on its accuracy and strong tolerance to noise on the results, UCB-1 is the right option for MAB for a low base conversion, a limited impact size scenario.

Organization Benefit as an Outcome of Organizational Security Adoption: The Role of Cyber Security Readiness and Technology Readiness

“…Hasan et al [5] also stated that to fully study the impact of all the main factors affecting cyber security and the importance of each factor, a comprehensive framework that combines these factors is needed. It is also confirmed in different studies that to understand the various factors that can improve organizational information security from various perspectives [61,62], it is not enough to consider only one aspect, but a more comprehensive model is needed that combines all the main factors that can affect the organization's information security organizational cyber security [63].…”

“…Organizations need to measure the performance of the organization's internal processes because this is an important part of organizational performance. Therefore, internal process safety should be measured in this study [63]. Tsuu and Hsu [22] added an item in measuring organizational performance, namely a good reputation so that in this study security reputation becomes an item that needs to be measured.…”

A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem

“…Basically the main idea is to always choose a heuristic with the highest upper bound. Therefore, it uses the uncertainty in the estimation of the value of the machines to balance exploration and exploitation, which is one of the most critical characteristics of UCB1 (Umami and Rahmawati, 2021).…”

A Data Mining Practical Approach to Inventory Management and Logistics Optimization

“…Using the principles of graph theory and a model based on a heuristic approach, elik and Süral [25] investigate the order picking problem (OPP) to find the path that minimizes the time necessary for storage operations. Matthews and Visagie [26] provide a proposal for reducing collection transfer times in order to obtain an adequate arrangement for SKU collection activities in a warehouse. Faia Pinto and Nagano [27] offer GA-OPS, a computational tool based on two genetic algorithms that reduces the number of picking trips while matching the constraints specified in different production orders.…”