Sequential decision-making problems in online marketing campaigns: optimisation and learning

This thesis investigates sequential decision-making in online marketing, focusing on the applications of multi-armed bandits (MABs) in reward-based crowdfunding and online advertising, with the aim of supporting marketing practitioners in effectively managing trade-offs in decision-making to maximise total revenue.

Institutions

Loughborough University (GB)

Publication Details

Journal: Figshare
Published: 2025-01-01
DOI: https://doi.org/10.26174/thesis.lboro.28490306.v1
Primary Topic: Advanced Bandit Algorithms Research
Type: article
Field-Weighted Citation Impact: 0.00

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article

Sequential decision-making problems in online marketing campaigns: optimisation and learning

Jan 1, 2025

Figshare

Advanced Bandit Algorithms Research

article

Sequential decision-making problems in online marketing campaigns: optimisation and learning

January 1, 2025article en

Abstract

Figshare

Loughborough University (GB)

Peace, Justice and strong institutions

Openalex Percentile: Top 53%

Advanced Bandit Algorithms Research

DOI Source OpenAlex

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Sequential decision-making problems in online marketing campaigns: optimisation and learning · Figshare (2025) | TGRS Research Map | TGRS