Högskolan i Skövde

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  • 1.
    Sahlin, Johannes
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. University of Borås, Sweden.
    Sundell, Håkan
    University of Borås, Sweden.
    Alm, Håkan
    University of Borås, Sweden.
    Holgersson, Jesper
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Evaluating Artificial Short Message Service Campaigns through Rule Based Multi-instance Multi-label Classification2021In: Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021): Stanford University, Palo Alto, California, USA, March 22-24, 2021 / [ed] A. Martin; K. Hinkelmann; H.-G. Fill; A. Gerber D. Lenat; R. Stolle; F. van Harmelen, CEUR-WS , 2021, Vol. 2846Conference paper (Refereed)
    Abstract [en]

    Marketers need new ways of generating campaigns artificially for their marketing activities. Many marketers assume proprietary systems are individualized enough. This article investigates an order of models used to measure how reliably a system can generate campaigns artificially while producing a campaign classification and generation models that are integrated into an intelligent marketing system. The order is between a Classification Model (CM) and a Generation Model (GM). The order also functions as an iterative model improvement process for developing the models by evaluating the models’ accuracy distributions. The CM received a mean accuracy of 100%. The GM received 98.9% mean accuracy and a reproducibility score of 96.2%, implying the vast potential for increased resource savings, marketing precision, and less consumer annoyance. The conclusion is that the developed system can reliantly construct campaigns

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  • 2.
    Sahlin, Johannes
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Department of Information Technology, University of Borås, Sweden.
    Sundell, Håkan
    Department of Information Technology, University of Borås, Sweden.
    Gideon, Mbiydzenyuy
    Department of Information Technology, University of Borås, Sweden.
    Alm, Håkan
    Department of Information Technology, University of Borås, Sweden.
    Holgersson, Jesper
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Suhonen, Christoffer
    Department of Information Technology, University of Borås, Sweden.
    Hjelm, Tommy
    Department of Information Technology, University of Borås, Sweden.
    Exploring Consumers' Discernment Ability of Autogenerated Advertisements2023In: 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. 322-329Conference paper (Refereed)
    Abstract [en]

    Autogenerated Advertisements (AGAs) can be a concern for consumers if they suspect that Artificial Intelligence (AI) was involved. Consumers may have an opposing stance against AI, leading companies to miss profit opportunities and reputation loss. Hence, companies need ways of managing consumers’ con-cerns. As a part of designing such advices we explore consumers’ discernment ability (DA) of AGAs. A quantitative survey was used to explore consumers’ DA of AGAs. In order to do this, we administered questionnaires to 233 re-spondents. A statistical analysis including Z-tests, of these responses suggests that consumers can hardly pick out AGAs. This indicates that consumers may be guessing and thus do not possess any significant DA of our AGAs.

  • 3.
    Sahlin, Johannes
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Department of Information Technology, University of Borås, Sweden.
    Sundell, Håkan
    Department of Information Technology, University of Borås, Sweden.
    Mbiydzenyuy, Gideon
    Department of Information Technology, University of Borås, Sweden.
    Holgersson, Jesper
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Scoped Literature Review of Artificial Intelligence Marketing Adoptions for Ad Optimization with Reinforcement Learning2023In: 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 (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.

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