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A Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution Algorithm

Budget allocation across multiple advertising channels involves periodically dividing a fixed total budget among various channels. Yet, the challenge of making sequential decisions to optimize long-term benefits rather than short-term gains is often overlooked. Additionally, more apparent connection...

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Main Authors: Li, Mengfan, Zhang, Jian, Alizadehsani, Roohallah, Plawiak, Pawel
Format: Article
Published: Institute of Electrical and Electronics Engineers 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/47093/
https://doi.org/10.1109/ACCESS.2024.3429359
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spelling my.um.eprints.470932024-11-22T04:43:01Z http://eprints.um.edu.my/47093/ A Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution Algorithm Li, Mengfan Zhang, Jian Alizadehsani, Roohallah Plawiak, Pawel QA75 Electronic computers. Computer science Budget allocation across multiple advertising channels involves periodically dividing a fixed total budget among various channels. Yet, the challenge of making sequential decisions to optimize long-term benefits rather than short-term gains is often overlooked. Additionally, more apparent connections must be made between actions taken on one advertising channel and the outcomes on others. Furthermore, budget limitations narrow down the range of potential optimal strategies that can be pursued. In response to these challenges, this study unveils a pioneering multi-channel advertising budget allocation approach that leverages a reinforcement learning (RL) Q-learning framework enriched with an advanced Differential Evolution (DE) algorithm to refine the Q-learning methodology. The RL element makes informed sequential decisions, adeptly adjusting strategies to favor long-term rewards by assimilating environmental feedback. Complementing this, the enhanced DE algorithm introduces an inventive clustering-based mutation technique, exploiting key groupings within the DE population to generate novel and practical solutions. The model is further bolstered by a discretization tactic aimed at simplifying the model by streamlining costs. The proposed methodology is rigorously validated using two extensive datasets: the Chinese Internet Company Advertising Dataset (CICAD) and CRITEO-UPLIFT v2, employing metrics like Area Under the Cost Curve (AUCC) and Expected Outcome Metric (EOM) as measures of performance. The empirical results affirm the superiority of the model, showcasing its exceptional performance with significant scores (AUCC = 0.750 and EOM = 0.736 for CICAD; AUCC = 0.813 and EOM = 0.829 for CRITEO-UPLIFT v2), thereby illustrating the model's proficiency in navigating the multifaceted challenges associated with multi-channel budget allocation and establishing a new benchmark in the field. Institute of Electrical and Electronics Engineers 2024 Article PeerReviewed Li, Mengfan and Zhang, Jian and Alizadehsani, Roohallah and Plawiak, Pawel (2024) A Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution Algorithm. IEEE Access, 12. pp. 100559-100580. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3429359 <https://doi.org/10.1109/ACCESS.2024.3429359>. https://doi.org/10.1109/ACCESS.2024.3429359 10.1109/ACCESS.2024.3429359
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Li, Mengfan
Zhang, Jian
Alizadehsani, Roohallah
Plawiak, Pawel
A Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution Algorithm
description Budget allocation across multiple advertising channels involves periodically dividing a fixed total budget among various channels. Yet, the challenge of making sequential decisions to optimize long-term benefits rather than short-term gains is often overlooked. Additionally, more apparent connections must be made between actions taken on one advertising channel and the outcomes on others. Furthermore, budget limitations narrow down the range of potential optimal strategies that can be pursued. In response to these challenges, this study unveils a pioneering multi-channel advertising budget allocation approach that leverages a reinforcement learning (RL) Q-learning framework enriched with an advanced Differential Evolution (DE) algorithm to refine the Q-learning methodology. The RL element makes informed sequential decisions, adeptly adjusting strategies to favor long-term rewards by assimilating environmental feedback. Complementing this, the enhanced DE algorithm introduces an inventive clustering-based mutation technique, exploiting key groupings within the DE population to generate novel and practical solutions. The model is further bolstered by a discretization tactic aimed at simplifying the model by streamlining costs. The proposed methodology is rigorously validated using two extensive datasets: the Chinese Internet Company Advertising Dataset (CICAD) and CRITEO-UPLIFT v2, employing metrics like Area Under the Cost Curve (AUCC) and Expected Outcome Metric (EOM) as measures of performance. The empirical results affirm the superiority of the model, showcasing its exceptional performance with significant scores (AUCC = 0.750 and EOM = 0.736 for CICAD; AUCC = 0.813 and EOM = 0.829 for CRITEO-UPLIFT v2), thereby illustrating the model's proficiency in navigating the multifaceted challenges associated with multi-channel budget allocation and establishing a new benchmark in the field.
format Article
author Li, Mengfan
Zhang, Jian
Alizadehsani, Roohallah
Plawiak, Pawel
author_facet Li, Mengfan
Zhang, Jian
Alizadehsani, Roohallah
Plawiak, Pawel
author_sort Li, Mengfan
title A Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution Algorithm
title_short A Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution Algorithm
title_full A Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution Algorithm
title_fullStr A Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution Algorithm
title_full_unstemmed A Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution Algorithm
title_sort multi-channel advertising budget allocation using reinforcement learning and an improved differential evolution algorithm
publisher Institute of Electrical and Electronics Engineers
publishDate 2024
url http://eprints.um.edu.my/47093/
https://doi.org/10.1109/ACCESS.2024.3429359
_version_ 1817841974361718784
score 13.235362

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