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Scoped Literature Review of Artificial Intelligence Marketing Adoptions for Ad Optimization with Reinforcement Learning
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Department of Information Technology, University of Borås, Sweden.ORCID iD: 0000-0002-3553-5983
Department of Information Technology, University of Borås, Sweden.ORCID iD: 0000-0003-4308-434X
Department of Information Technology, University of Borås, Sweden.ORCID iD: 0000-0002-9685-7775
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Information Systems)ORCID iD: 0000-0002-8900-6139
2023 (English)In: Machine Learning, Multi Agent and Cyber Physical Systems: Proceedings of the 15th International FLINS Conference (FLINS 2022) / [ed] Qinglin Sun; Jie Lu; Xianyi Zeng; Etienne E. Kerre; Tianrui Li, World Scientific, 2023, p. 416-423Conference paper, Published paper (Refereed)
Abstract [en]

Artificial Intelligence (AI) and Machine Learning (ML) are shaping marketing activities through digital innovations. Competition is a familiar concept for any digital retailer, and the digital transformation provides hopes for gaining a competitive edge over competitors. Those who do not adopt digital innovations risk getting outcompeted by those who do. This study aims to identify AI mar-keting (AIM) adoptions used for ad optimization with Reinforcement Learning (RL). A scoped literature review is used to find ad optimization adoptions re-search trends with RL in AIM. Scoping this is important both to research and practice as it provides spots for novel adaptations and directions of research of digital ad optimization with RL. The results of the review provide several different adoptions of ad optimization with RL in AIM. In short, the major category is Ad Relevance Optimization that takes several different forms depending on the purpose of the adoption. The underlying found themes of adoptions are Ad Attractiveness, Edge Ad, Sequential Ad and Ad Criteria Optimization. In conclusion, AIM adoptions with RL is scarce, and recommendations for future research are suggested based on the findings of the review.

Place, publisher, year, edition, pages
World Scientific, 2023. p. 416-423
Series
World Scientific Proceedings Series on Computer Engineering and Information, ISSN 1793-7868, E-ISSN 2972-4465 ; 13
Keywords [en]
Advertisement, Artificial intelligence, Reinforcement learning
National Category
Computer Sciences
Research subject
Information Systems
Identifiers
URN: urn:nbn:se:his:diva-23233DOI: 10.1142/9789811269264_0049ISBN: 978-981-126-925-7 (print)ISBN: 978-981-126-927-1 (electronic)OAI: oai:DiVA.org:his-23233DiVA, id: diva2:1799048
Conference
Conference on Machine learning, Multi Agent and Cyber Physical Systems (FLINS 2022), Tianjin, China, 26 – 28 August 2022
Funder
Knowledge Foundation
Note

Partly funded by The Knowledge Foundation, grants nr. 20160035, 20170215

Available from: 2023-09-21 Created: 2023-09-21 Last updated: 2023-10-10Bibliographically approved
In thesis
1. Designing Advertisement Systems with Human-centered Artificial Intelligence
Open this publication in new window or tab >>Designing Advertisement Systems with Human-centered Artificial Intelligence
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Practitioners are urging using Artificial Intelligence (AI) to improve advertisements. Advertisers recognize the importance of incorporating AI into their strategies to remain competitive. In response to this demand, a Design Science Research (DSR) initiative has been started to create a Human-Centered AI (HCAI) tool to enhance advertisement suggestions by analyzing consumer behavior. This dissertation aims to build an advertisement optimization system with HCAI and produce an Information System Design Theory (ISDT) of that class of system. Through architectural models, methods, technological rules, and design principles, nascent Design Theory (DT) is created and serves as an initial stage towards achieving a more abstract design knowledge known as the ISDT. The action design research method is employed to construct and analyze the implemented system instance. The process involves multiple cycles of building, intervening, and evaluating. These cycles are conducted iteratively and incrementally, allowing for the gradual development of the suggested system while simultaneously generating valuable design knowledge. The system is developed and abstracted for design knowledge from both the development process and the actual tool. The dissertation presents nascent design knowledge in the form of models, technological rules, and design principles. Moreover, the dissertation places the nascent DT within the broader context of a more abstract design knowledge called ISDT. The results are then scrutinized based on various components of the DT, including purpose and scope, constructs, principles of form and function, artifact mutability, justificatory knowledge, testable propositions, principles of implementation, and expository instantiation. This dissertation discusses the DSR process, compared to various challenges encountered throughout the research project. Theoretical, empirical, and artefactual research contributions are outlined, and their implications for research and practice are discussed toward the end of the dissertation. The quality of the research is examined, considering the relevance, novelty, usefulness, feasibility, design rigor, evaluation rigor, and transparency of the artifacts produced throughout the dissertation. The dissertation concludes that it delivered ISDT. Moreover, the system serves as a valuable example of how AI can be utilized for optimizing digital advertisements. The dissertation ends with providing recommendations for future research.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2023. p. xiv, 361
Series
Dissertation Series ; 54
Keywords
Digital Advertisement Optimization, Design Knowledge, Information System Design Theory, Artificial Intelligence, Reinforcement Learning, Human-centered AI
National Category
Computer Sciences
Identifiers
urn:nbn:se:his:diva-23234 (URN)978-91-987906-8-9 (ISBN)
Public defence
2023-11-01, Insikten, Kanikegränd 3B, Skövde, 10:00
Opponent
Supervisors
Funder
Knowledge Foundation, 20160035, 20170215
Note

Partly funded by The Knowledge Foundation, grants nr. 20160035, 20170215

Två av sex delarbeten (övriga se rubriken Delarbeten/List of papers):

Sahlin, Johannes, Håkan Sundell, Gideon Mbiydzenyuy, and Jesper Holgersson (2023). “Managing Consumer Concerns of Model-generated Advertisements.” In: Expert Systems with Applications, Submitted.

— (2024). “Nascent Design Theory for Advertisement Optimization Systems with Human-centered Artificial Intelligence.” In: European Conference on Information Systems, Draft.

Available from: 2023-10-02 Created: 2023-09-21 Last updated: 2023-10-03Bibliographically approved

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Sahlin, JohannesHolgersson, Jesper

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