Högskolan i Skövde

his.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Scoped Literature Review of Artificial Intelligence Marketing Adoptions for Ad Optimization with Reinforcement Learning
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningsmiljön Informationsteknologi. 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
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningsmiljön Informationsteknologi. (Information Systems)ORCID-id: 0000-0002-8900-6139
2023 (engelsk)Inngår i: 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, s. 416-423Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
World Scientific, 2023. s. 416-423
Serie
World Scientific Proceedings Series on Computer Engineering and Information, ISSN 1793-7868, E-ISSN 2972-4465 ; 13
Emneord [en]
Advertisement, Artificial intelligence, Reinforcement learning
HSV kategori
Forskningsprogram
Informationssystem (IS)
Identifikatorer
URN: urn:nbn:se:his:diva-23233DOI: 10.1142/9789811269264_0049ISBN: 978-981-126-925-7 (tryckt)ISBN: 978-981-126-927-1 (digital)OAI: oai:DiVA.org:his-23233DiVA, id: diva2:1799048
Konferanse
Conference on Machine learning, Multi Agent and Cyber Physical Systems (FLINS 2022), Tianjin, China, 26 – 28 August 2022
Forskningsfinansiär
Knowledge Foundation
Merknad

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

Tilgjengelig fra: 2023-09-21 Laget: 2023-09-21 Sist oppdatert: 2025-09-29bibliografisk kontrollert
Inngår i avhandling
1. Designing Advertisement Systems with Human-centered Artificial Intelligence
Åpne denne publikasjonen i ny fane eller vindu >>Designing Advertisement Systems with Human-centered Artificial Intelligence
2023 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Skövde: University of Skövde, 2023. s. xiv, 361
Serie
Dissertation Series ; 54
Emneord
Digital Advertisement Optimization, Design Knowledge, Information System Design Theory, Artificial Intelligence, Reinforcement Learning, Human-centered AI
HSV kategori
Identifikatorer
urn:nbn:se:his:diva-23234 (URN)978-91-987906-8-9 (ISBN)
Disputas
2023-11-01, Insikten, Kanikegränd 3B, Skövde, 10:00
Opponent
Veileder
Forskningsfinansiär
Knowledge Foundation, 20160035, 20170215
Merknad

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.

Tilgjengelig fra: 2023-10-02 Laget: 2023-09-21 Sist oppdatert: 2025-09-29bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstFulltext (DiVA HB)

Person

Sahlin, JohannesHolgersson, Jesper

Søk i DiVA

Av forfatter/redaktør
Sahlin, JohannesSundell, HåkanMbiydzenyuy, GideonHolgersson, Jesper
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 380 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf